# A Marketplace for Trading AI Models based on
Blockchain and Incentives for IoT Data

Lam Duc Nguyen, IEEE Graduate Member, Shashi Raj Pandey, IEEE Member, Soret
Beatriz, IEEE Member,
Arne Bröring, and Petar Popovski, IEEE Fellow Lam Duc Nguyen, Shashi Raj
Pandey, Soret Beatriz, and Petar Popovski are with Connectivity Section,
Electronic System, Aalborg University, Denmark. Email: {ndl, srp, bsa,
<EMAIL_ADDRESS>Bröring is Senior Key Expert Research Scientist with
Siemens AG, Munich, Germany. Email<EMAIL_ADDRESS>
###### Abstract

As Machine Learning (ML) models are becoming increasingly complex, one of the
central challenges is their deployment at scale, such that companies and
organizations can create value through Artificial Intelligence (AI). An
emerging paradigm in ML is a federated approach where the learning model is
delivered to a group of heterogeneous agents partially, allowing agents to
train the model locally with their own data. However, the problem of valuation
of models, as well the questions of incentives for collaborative training and
trading of data/models, have received a limited treatment in the literature.
In this paper, a new ecosystem of ML model trading over a trusted Blockchain-
based network is proposed. The buyer can acquire the model of interest from
the ML market, and interested sellers spend local computations on their data
to enhance that model’s quality. In doing so, the proportional relation
between the local data and the quality of trained models is considered, and
the valuations of seller’s data in training the models are estimated through
the distributed Data Shapley Value (DSV). At the same time, the
trustworthiness of the entire trading process is provided by the Distributed
Ledger Technology (DLT). Extensive experimental evaluation of the proposed
approach shows a competitive run-time performance, with a 15% drop in the cost
of execution, and fairness in terms of incentives for the participants.

###### Index Terms:

Blockchain, Federated Learning, Model Trading, Data Valuation, Shapley Value.

##  I Introduction

Personal IoT devices keep generating an enormous amount of sensing data that
is expected to reach 79.4 Zettabytes (ZB) globally in 2025[1]. Several
attempts to enhance and adapt business workflows have been made towards
exploiting the provision of IoT data[2, 3]. In this regard, training machine
learning models and data sharing are two popular uses of IoT data.
Furthermore, emerging diverse platforms for accessing and sharing IoT data
connects various distributed IoT devices/data sources, thereby facilitating
suppliers to exchange their data [4].

For example, in IoT systems for air quality monitoring and emission control,
Air Quality Index (AQI) is a quantity defined to estimate the degree of
severity for air pollution and CO2 emission levels. AQI quantifies the
concentration of various particles in the air, such as PM2.5 or PM5.0, using
state-of-the-art sensor devices [5]. There are two most popular measurement
methods for AQI: i) sensing-based [6], and ii) vision-based [7]. In the
sensing-based method, the IoT sensor devices are delivered around the area
interest, e.g., city, urban, to collect the quality of the air and emission
levels. These measurements are then forwarded to the central server for
further analysis and calculation of AQI. In the vision-based method, the
devices with an embedded camera, such as a camera station in the road, or
individual mobile phones, can take photos of a specific area and send them to
the server. The server then applies advanced image processing techniques on
these images to derive the analysis report of air quality. However, both
methods have problems due to (i) high energy consumption for collecting data
and transmission, (ii) requirement for a large dataset for a high quality AQI
estimation, (iii) the server acting as a single point of failure, and (iv)
data privacy concerns under General Protection Regulation (GDPR). Several
recent works have addressed issues related to (i) using efficient resource
management techniques and (ii) with dense sensory networks [8]. However, the
primary concerns about (iii) and (iv) remain a single point of failure network
topology and data privacy protection. They are yet to be addressed in an
efficient manner. In addition, the regulations such as EU’s GDPR, California’s
Consumer Privacy Act (CCPA), and China’s Cyber Security Law (CSL)[9] limits
the reckless use/collection of personal information and fosters data privacy.
Hence, a feasible joint solution to address challenges raised in (iii) and
(iv) is imperative for optimized operation of the market for data exchange.

Figure 1: A motivation example: IoT devices contribute to train an ML model
for the buyer to predict CO2 emission levels and get incentives from their
contributions.

In this regard, more recently, Federated Learning (FL) has been considered a
key solution to address the privacy issue in training learning models [10]. FL
is a distributed model training paradigm that aims to solve the challenges of
data governance and privacy by training algorithms collaboratively rather than
transferring the data itself. For example, in a typical FL setting, at first,
as shown in Fig. 1, the IoT devices collect the pollution and CO2 emission
levels and store them in their local database. Consider an interested
organization or individual, termed a buyer, willing to train an ML model to
predict a specific area’s emission level. However, they do not possess
sufficiently large datasets about sensing information or image data. In such a
scenario, they can send their initial model to a model marketplace to find
appropriate parties interested in contributing to the model training process.
Then, the IoT devices, termed sellers, can download the initial model and
train it using their local data. After that, the IoT devices can share the
updated model weights to the marketplace, where there is an aggregator to
aggregate submitted local models to build global models. Based on the
aggregated global models, the model buyer can use the global model to predict
the emission levels with acceptable precision. In this manner, using the FL
approach, we can address the problem of data privacy where data is locally
trained without the need to transmit to a central server. However, from a
systems perspective, a shared IT environment, such as an aggregator in the
marketplace, may become a single point of failure in terms of data integrity,
trust, security, and transparency [11].

A conventional data market is often deployed as a centralized service platform
that gathers and sells raw or processed data from data owners (e.g., the
trained learning models) to the consumers [12] [13]. This leads to two
important concerns. _First_ , this strategy exposes the platform as a single
point of security risk; the malfunctioning platform servers has serious
security concerns including data leakage, inaccurate calculations results, and
manipulation of data price. _Second_ , collaboration for model training raises
questions in terms of how to motivate participants to participate in such an
ML training endeavor. The incentive for each IoT client based on their
contribution should be fair and transparent. These features are not present in
a standard FL setup, as in many applications there is no clear and natural
incentive mechanism for involved participants to provide quality information.
This calls for a carefully designed mechanisms to reward parties economically
and thus incentivize participation [14, 15]. For example, a fixed price per
data point could motivate participants to collect massive amounts of low
quality or fake data if there is no intermediary process to check quality of
training data. Besides, another reason that may disincentives parties from
sharing data could stem from privacy and integrity concerns regarding the use
of participant’s data once it is shared. For instance, the sellers can re-use
the data which has already been sold.

The aforementioned challenges can be effectively handled by a Distributed
Ledger Technologies (DLTs).111In this work, the terms _Blockchain_ and DLT are
used interchangeably. Blockchains are a type of DLT, where nodes maintain a
copy of the ledger having embedded chains of blocks. These blocks are
basically composed of digital pieces of information, particularly defined as
transactions. DLTs and Blockchains enable untrusted parties to share
information in an immutable and transparent manner[16]. Outside of its key
role in financial transactions, the applications of DLTs can be seen as a key
enabler for trusted and reliable distributed IoT systems, e.g., a distributed
IoT data marketplace For instance, in a Blockchain-enabled IoT data
marketplace [17], Blockchain transactions include IoT sensing data, or system
control messages, and these are recorded and synchronized in a distributed
manner in all the involved participants of the network[18]. Furthermore, DLTs
enable the preservation of all transactions in immutable records, with each
record being spread across several participants. Thus, the decentralized
nature of DLTs ensures security, as does the use of robust public-key
encryption and cryptographic hashes. The advantages of incorporating DLTs into
trading AI models in IoT systems include: i) ensuring immutability and
transparency for historical AI model trading records, ii) eliminating the need
for third parties, and iii) developing a transparent system for AI model
trading in heterogeneous networks to prevent tampering and injection of fake
data from the stakeholders, according to [19, 20]. With the wide spread of
ubiquitous marketplaces recently, it became relevant to investigate the use of
AI/ML model trading in marketplace environments.

With the aforementioned motivation, we propose a Blockchain-based model
trading system which enables a secured and trusted marketplace to
collaboratively train ML models as well as guarantees fair incentives for
every participants and privacy of data. Based on the quality of the uploaded
models, which is quantified by using a distributed Data Shapley Value (DSV),
the participants222The terms “participants”,“clients” and “agents” in this
work are used to refer to “IoT devices”. can get the incentive based on the
updated models, for example, as tokens or fiats. Note that based on our
proposed system, the parties do not need to share their local data, but only
provide customized models or query interface to the marketplace. Consequently,
the proposed system allows multiple participants to jointly train the ML
models on the marketplace based on their own training data. Buyers who need to
train their ML model will pay to the market for the improvement of their
model, and sellers who sell their contribution to train the ML models will get
paid by the market via smart contracts.

The main features of the proposed model trading are:

  1. 1.

Trusted and transparent transactions: The DLT is considered a trusted, tamper-
proof, and transparent system in which the participants can check and follow
the progress of a training task. Based on that, the model is exchanged and
traded securely and transparently.

  2. 2.

Valuation of Data: The local models contributed by the trainers (service
sellers) are collected and evaluated via Shapley Value (SV) extension to
approximately estimate the quality of the models.

  3. 3.

Fair Payment: The participants receive their reward, which is proportional to
the usefulness of their data in improving the models. The distributed
incentive mechanism for FL based on SV measures participants’ contributions in
the marketplace.

###  I-A Contributions and Paper Organization

The major contributions are summarized as follows:

  * •

ML Model Marketplace: A Blockchain-based model trading system that allows
participants to purchase learning models and sell contributions in training
them. The system records the trading details [17] in a tamper-proof
distributed ledger.

  * •

Federated Data Shapley Value (SV):  We use the data SV to estimate the
valuation of participants’ data and evaluate their contribution to the model
during local training. We show that the standard SV value is inefficient for
distributed ML and deploy an extension of standard SV for our platform. The
method is robust and allows plugging any developed mapping functions related
to the device’s local data into the proposed distributed Shapley mechanism for
value quantification. As a result, one can design a contribution-based,
efficient incentive mechanism to stimulate model trading.

  * •

Performance Evaluation: We have conducted extensive simulations and
experiments, demonstrating that the proposed approach shows a competitive run-
time performance, with a 15% drop in the cost of execution and fairness in
terms of incentives for the participants.

The rest of the article is organized as follows. Section II, presents the
concepts of DLTs, FL, as well as definition of data valuation schemes used in
this paper. This is followed by description of the system model of the
marketplace for ML model trading and explain in detail how the system works.
In Section III, the value of ML models is calculated using the _Approximate
Federated Shapley Value (AFS)_. Section IV contains description of the testbed
and experimental results. Section V discusses related works and finally,
Section VI concludes the paper.

##  II Preliminaries

###  II-A Standard FL

FL is a distributed machine learning setting in which numerous entities
(clients) cooperate on training a learning model without disclosing their
available raw data [10]. Instead, clients distributively perform computations
on their data and transfer obtained local learning parameters updates to the
server for aggregation process. The aggregated model, i.e., the global model,
is broadcast back to the clients for the next round of local computations
resulting in the local learning parameters. The interaction between the server
and clients to solve the learning problem continues until an acceptable level
of model accuracy is achieved [21]. In this manner, FL offers (i) privacy-
preserving benefits in the model training approach by not requiring clients to
share their local data to the server, and consequently, (ii) lower
communication overhead by offering distributed model training paradigm and
exchange of model parameters only. Therein, FL enables training AI/ML models
at edge networks

Figure 2: Standard FL. TABLE I: Summary of key notations. Notation |  Meaning
---|---
$MI_{i}$ |  DLT miner $i$
$\mathcal{N}$ |  Set of $N$ participants (clients)
$D_{i}$ |  Private local dataset of user $i\in\mathcal{N}$
$\mbox{\bf{M}}_{i}$ |  Local model of user $i\in\mathcal{N}$
$\mathcal{M}_{G}$ |  Global model aggregated at DLTs
$\mathcal{M}^{0}_{G}$ |  Initial Global model at DLTs
$L(\mbox{\bf{M}}^{t}_{i})$ |  Loss function
$\eta$ |  Learning rate
$S_{i}$ |  Model trainer (or Seller) $i$
$B_{i}$ |  Model owner (or Buyer) $i$
$\mathcal{P}_{d}$ |  Deposit from buyer
B |  Training batch size
$\mathcal{A}$ |  Training algorithm
$U(\cdot)$ |  Utility function
$\phi_{i}$ |  Valuation of data contributor $i$
$E$ |  Number of local epochs for a FL setting
$T$ |  Number of training interactions
$\mathcal{T}_{i}$ |  A trade deal between $S_{i}$ and $B_{i}$
$\widetilde{\mathcal{M}}$ |  Approximated model $\widetilde{\mathcal{M}}$

Fundamentally, there exists two main actors in the FL system: (i) the data
owners, often termed as participants, and (ii) the model owner, which is the
FL server. Consider a set of
${\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}N}$
data owners, defined as $\mathcal{N}=\\{1,2,\ldots,N\\}$, where each of them
has a private dataset $D_{i\in\mathcal{N}}$. In Table I, we provide the
summary of key notations used in this paper. Each data owner
${\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}i}$
trains a local model $\mbox{\bf{M}}_{i}$ using its dataset $D_{i}$ and sends
only the obtained local model parameters to the FL server. Then, the FL server
aggregates all the collected local models to build a global model,
$\mathcal{M}_{G}=\sum_{i\in\mathcal{N}}M_{i}$. This is where, in principle,
the FL approach differs from the traditional centralized training where
$D=\cup_{i\in\mathcal{N}}D_{i}$ is used to train a model $\mathcal{M}_{T}$,
i.e., data first gets aggregated centrally before the actual model training
happens. In Fig. 2, we show a standard architecture and an overview mechanism
of the FL training process. We assume that the data owners are honest, i.e.,
actual private data will be used for the local training, and correspondingly,
the FL server will receive accurate local models from the data owners.
Following to which, the workflow of standard FL can be described as below.

First, considering the target application, the server decides the training
task and defines the corresponding data requirements. Furthermore, the server
also specifies the hyper-parameters of the global model and the training
process, e.g., the learning rate $\eta$. The server then broadcasts the
initialized global model $\mathcal{M}^{0}_{G}$ and the learning task to a
subset of selected participants. Next, based on the global model
$\mathcal{M}^{t}_{G}$, where $t$ denotes the current global iteration index,
i.e., the communication rounds between the participants and the server, each
participant uses its local data to update their model parameters $M^{t}_{i}$.
In doing so, during iteration $t$, the participant $i\in\mathcal{N}$ aims at
finding the optimal parameters $\mathcal{M}^{t}_{i}$ that minimize the local
loss problem $L(M^{t}_{i})$, defined as the finite-sum of empirical risk
functions as

$\mathcal{M}^{t}_{i}=\operatorname*{arg\,min}_{M^{t}_{i}}L(M^{t}_{i}).$ (1)

We formally call it local iteration. Subsequently, the obtained local model
parameters from participants are sent back to the server, where they are
aggregated to get the global model parameters $M^{t+1}_{G}$. Eventually, the
global model is then broadcast back to the data owners for the next round of
local iteration; hence, the iterative process is continued. In doing so, the
server minimizes the global loss function $L(M^{t}_{G})$ as the following
approximation in the distributed setting of FL:

1

Input: Local mini-batch size B, number of participants per interaction $m$,
number of global interactions $T$, number of local epochs $E$, and learning
rate $\eta$

. Output: Global Model $\mathcal{M}_{G}$

2. LocalTraining($i$, M): Split local dataset $D_{i}$ to mini-batches of size B which are included into the set $\mathcal{B}$.

3 for  _each local epoch $j$ from $1$ to $E$_ do

4 for  _each
$b\in{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathcal{B}}$_
do

5
$\mbox{\bf{M}}\leftarrow\mbox{\bf{M}}-{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\eta\nabla
L(\mbox{\bf{M}};b)}$;

6

7

8Return M to the server.

9

10Initialized $\mathcal{M}_{G}^{0}$.

11 for  _each interaction $t=\\{0,1,2,3,\ldots,T-1\\}$_ do

12

13
${\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mathcal{S}_{t}}\leftarrow$
(random set of $m$ clients);

14 for  _ each participant $i\in\mathcal{S}_{t}$ in parallel _ do

15
${\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}\mbox{\bf{M}}_{i}}^{t+1}\leftarrow\textbf{LocalTraining}(i,\mathcal{M}_{G}^{t})$;

16

17
$\mathcal{M}_{G}^{t+1}=\frac{1}{\sum_{i\in\mathcal{N}}D_{i}}\sum_{i=1}^{N}D_{i}\mbox{\bf{M}}_{i}^{t+1}$;

18

Algorithm 1 Federated Averaging (FedAvg) Algorithm

$L\left({\mathcal{M}_{G}^{t}}\right)=\frac{1}{N}\sum\nolimits_{i=1}^{N}L\left({\mathcal{M}_{i}^{t}}\right).$
(4)

Note that the FL process can train different ML models that essentially use
the SGD method such as Support Vector Machines (SVMs), neural networks, and
linear regression.

However, a single server dependency in the traditional FL framework makes the
system vulnerable to threats, such as when the server behaves maliciously.
Therefore, integrating FL with DLTs should be a promising approach to address
limitations[22].

Figure 3: The accuracy of Standard FL and DLT-based FL.

###  II-B Distributed Ledger as a Service for FL

DLT is a peer-to-peer distributed ledger that records transactions in a
network in a transparent and immutable manner. Besides, smart contract, which
is considered as a key innovation in DLT/Blockchain area, provide
programmability contracts to the DLTs, in the sense that the defined
agreements in contracts are executed autonomously. With the mentioned nature
advantages of DLTs and smart contract, the FL framework running on the top of
DLT should be completely distributed and avoid the single point of failure
issue.

In the DLT-based FL, we assume each client device is always connected to one
of the DLT miners and, if the physical connection with the current DLT miner
becomes unavailable, then the device will be automatically associated with
another DLT miner. In each miner-device pair, the DLT miner works as the
leader of the associated IoT devices, and they are responsible for uploading
and downloading data or training models. During the training process, the IoT
device downloads the latest global model recorded in the ledger and trains for
the updated version of the local model using their private local data. After
completing the local training, the device uploads the local model to the
paired DLT miner and the global aggregation process starts. In the training
time, all involved IoT devices are allow to download the latest information of
associated DLT miners to receive the evaluation of the IoT devices and global
model updates. Finally, each IoT device publishes its local training model and
enters to a new round of local iteration using the newest version of the
obtained global model. In this manner, the iterative ML model training process
is operated until the global model has achieved a satisfactory accuracy or
convergence.

Each miner has its verifier and block to ensure that the real models and the
contributions of devices are updated. Each block contains a head and body
parts. The blockhead contains a pointer to the next block, and the body part
contains a set of validated transaction information. The local models are
formed in transaction format and in order to make the solution scalable, the
local models are recorded in IFPS storage, such that just a hash version of
the models is recorded in the distributed ledger. The basic comparison between
standard FL and DLT-based FL is presented in Fig.3. The accuracy is similar in
both standard FL and DLT-based FL, but the time required for convergence of
DLT-based FL is higher than standard FL because of extra verification and
consensus in the system.

###  II-C Data valuation using Shapley value

Game theory is an economic tool best-suited to analyze a system where two or
more participants get involved in to achieve a desired payoff. The Shapley
Value (SV) is a solution concept of fairly distributing the incentive and
payoff for the involved parties in coalition [23]. In this regard, the SV
applies mainly in scenarios where the contributions of each involved
participant are unequal, but all the participants work in cooperation with
each other to achieve the payoff. The SV of user $i$ is defined as the average
marginal contribution of $i$ to all possible subsets of
$D=\\{D_{1},D_{2},\ldots,D_{N}\\}$ formed by other users as

$\phi_{i}(N,U)=\frac{1}{N!}\sum_{\mathcal{S}\subseteq\mathcal{N}\setminus\\{i\\}}\frac{U(M_{\mathcal{S}\cup\\{i\\}})-U(M_{\mathcal{S}})}{\binom{N-1}{|\mathcal{S}|}},$
(2)

where the function $U(\cdot)$ gives the value for any subset of those users,
e.g., let $\mathcal{S}$ be a subset of $\mathcal{N}$, then $U(\mathcal{S})$
gives the value of that subset. This captures the average value of the
contributions of user $i$ for subsets of all coalition of users. Intuitively,
assume that the user’s data is to be collected in a random order, and that
every user $i$ receives its marginal contribution for the collected data. If
we average these contributions over all the possible orders of $N$ users, we
obtain $\phi_{i}(N,U)$. The importance of the SV is that it is the unique
value division scheme that satisfies the following desirable properties
described as follows.

  * •

Symmetry: For all $\mathcal{S}\subseteq\mathcal{N}\setminus\\{i,j\\}$, if user
$i$ and $j$ are interchangeable, and
$U(\mathcal{S}\cup\\{i\\})=U(\mathcal{S}\cup\\{j\\})$, then,
$\phi_{i}=\phi_{j}$. Thus, the users $i$ and $j$ contribute the same amount to
every coalition of the other agents. Besides, the symmetry axiom states that
such agents should receive the same payments.

  * •

Dummy User: User $i$ is considered as dummy user if the amount that $i$
contributes to coalition is exactly the amount that $i$ is able to achieve
alone, i.e.,
$\forall\mathcal{S},i\notin\mathcal{S},U(\mathcal{S}\cup\\{i\\})-U(\mathcal{S})=U(\\{i\\})$.
According to the dummy user axiom, dummy users should be compensated exactly
for the amount they achieve on their own. Users that make zero marginal
contributions to all subsets of the data set, on the other hand, earn no
compensation, for example, $\phi_{i}=0$ if
$U(\mathcal{S}\cup\\{i\\})=0,\forall\mathcal{S}\subseteq\mathcal{N}\setminus\\{i\\}$.

  * •

Additivity: For any two $U_{1}$ and $U_{2}$, we have for any user $i$,
$\phi_{i}(N,U_{1}+U_{2})=\phi_{i}(N,U_{1})+\phi_{i}(N,U_{2})$, where the game
$(N,U_{1}+U_{2})$ is defined by
$(U_{1}+U_{2})(\mathcal{S})=U_{1}(\mathcal{S})+U_{2}(\mathcal{S})$ for every
coalition $\mathcal{S}$.

Based on these background knowledge, we designed a distributed marketplace for
trading AI/ML models based on Blockchain and incentive mechanism in IoT
environment.

Figure 4: DLT-based ML Model Trading Framework Architecture.

##  III System Design and Analysis

###  III-A System Components

The general architecture of DLT-based model trading includes three main
components: model owners or buyers $B$, model trainers or sellers $S$, and a
distributed ledger, shown in Fig. 4. We assume that each seller or buyer owns
one device in the network. Within a deal (a trade) by $\mathcal{T}_{i}$, the
seller $S_{i}\in S$ and buyer $B_{i}\in B$ communicate using wireless links.
The AI/ML model trading procedure occurs to complete a trade between $S_{i}$
and $B_{i}$, exchanging models $\mathcal{M}_{i}\in\mathcal{M}$ and payment
$\mathcal{P}_{i}$. First and foremost, $B_{i}$ completes the deposit
$\mathcal{P}_{d}$ to $S_{i}$ via smart contracts in reference to the requested
training models, $\mathcal{M}_{i}$. After the sellers complete the requests of
the buyers, in terms of accuracy, convergence time, etc, the smart contracts
are autonomous executed to pay for the effort of sellers using the amount of
deposit $\mathcal{P}_{d}$ from buyers. Following Fig. 4, the general procedure
of interaction between a single buyer $B_{i}$ and a single seller $S_{i}$ can
be described as follows:

####  III-A1 Model Owner $i$ (as buyer $B_{i}$)

$B_{i}$ could be an individual or organization who needs model training.
$B_{i}$ sends a request $b_{i}$ including task type, budget, deposit, amount
of data, quality of data, price, discount, etc, to the marketplace via smart
contracts. $b_{i}$ will be transmitted to selected $S_{i}$ and recorded in the
ledger via transaction $T_{i,\textrm{add}}$. After receiving the trained and
aggregated models from $S_{i}$ and marketplace which fulfills requirements
regarding to, e.g., accuracy, the $B_{i}$ generates a transaction
$T_{i,\textrm{commit}}$ which executes payment from $B_{i}$’s wallet to smart
contract, forwards the payment to sellers, and generates a acknowledgment
message back to the distributed ledger.

####  III-A2 Model Trainer (as seller $S_{i}$)

The model trainers play two main roles in the system: i) collects sensing data
from the environment (e.g., data from surveillance systems, environmental
sensing data, and geographical data), or acts as a data hub gathering data
from nearby physical devices; ii) subscribes the model training requests from
the buyers, and train the models downloaded from the marketplace with the
local data. Seller $S_{i}$ earns the payment $\mathcal{P}_{i}$ from $B_{i}$
after successful delivery of $M_{i}$ to $B_{i}$. After the trained models
achieve a certain accuracy based on the predefined agreements in the smart
contract system, upon the appearance of $T_{i,\textrm{commit}}$ generated by
$B_{i}$, the seller $S_{i}$ can receive the payment, e.g., via tokens,
$\mathcal{P}_{i}$, which is in fact the deposited amount $\mathcal{P}_{d}$ by
the buyer $B_{i}$, from the marketplace via smart contract. Finally, it
confirms to the distributed ledger that the deal $\mathcal{T}_{i}$ is
completed via an acknowledgment message.

####  III-A3 Distributed Ledgers

The Blockchain maintains a distributed ledger that stores the history of all
traded models in the form of blocks, which are connected in a chronological
order. On top of that, the smart contracts are deployed to autonomously
control the order and execute payments, e.g,. large payment or micro-payments
from involved participants without the need of human intervention. In a
distributed manner, the smart contracts ensure transparency, trust and
automotive of exchanging data among parties. These features can be deployed
based on the negotiation between model owners and customers via
$T_{i,\textrm{deploy}}$. Furthermore, any change in smart contracts, for
example, the amount of data or the model price, or updates in the discount
offers, can be made via $T_{i,\textrm{update}}$.

In a trading system, there are an enormous amount of data exchanged among
parties. Thus, increasing the number of transactions leads to slower
transaction processing time and, consequently, the system’s overall speed.
This is reasonable as every Blockchain node needs to store and execute a
computational task to validate every single transaction. Therefore, to
minimize the cost of storage and execution, the trading system should record
only the important data, such as payment history, aggregated global models,
which could be hashed and recorded at the distributed ledger. Meanwhile, the
raw data can be recorded in the distributed off-line storage component. In
detail, after both model sellers $S_{i}$ and model owners $B_{i}$ have
fulfilled requirements defined by smart contracts, the $T_{i,\textrm{settle}}$
is autonomously executed to query the payment $\mathcal{P}_{i}$ from $B_{i}$.
Then, the payment $\mathcal{P}_{i}$ is transferred to $S_{i}$’s private
wallet, while the aggregated model $\mathcal{M}_{i}$ is delivered to the
storage address of $B_{i}$. In the scope of this study, we assume that the
data services (e.g., data storage, trading and task dispatching) are
implemented on top of a permissionless Ethereum Blockchain [24]. In this work,
the control data and AI/ML models are formatted into normal Ethereum
transactions. Furthermore, in order to improve efficiency, only the digest of
each transaction is recorded in the distribute on-chain ledger, and the raw
data is stored off-chain by using IPFS (InterPlanetary File System).

###  III-B Communication Workflow

In the DLT-based FL model trading network, we revisit the notation used to
denote data owners and define the number of participants as
$\mathcal{N}=\\{1,2,3,\ldots,{\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}N}\\}$.
The miner $MI_{i}$ of DLT network is associated randomly with the IoT device.
For simplicity, we consider the case which one miner is assigned to each
physical IoT device. Each IoT device has to determine its own learning task-
related dataset size and upload it to the ledger system to receive a reward. A
distributed SV incorporates the quality of local data333The quality of data
signifies the size of dataset used in model training, similar to [25]. In this
study, we do not consider feature attributes of data to quantify its quality.
to determine the corresponding quality of the local model. We realize that, in
some case e.g healthcare, it would be better to extract features and measuring
the quality based on real quality and not quantity, but it is out of this
research scope. In addition, in resource-constraint IoT environments, to
reduce latency and optimize energy consumption, each device’s local controller
performs local optimizations to establish the best scheduling policy for
device resources, such as CPU cycles scheduling.

The workflow of the system is described as below.

Step 1 (Model Initialization): The buyer $B_{i}$ initiates a model
$\mathcal{M}_{i}$ which needs to be trained and publishes to the DLT-based
marketplace. The initial model is formed in the DLT transaction format
$T_{\textrm{publish}}$.

Step 2 (Publish initialized model to the ledger): Then, the transaction
$T_{\textrm{publish}}$ including initialized model $\mathcal{M}_{i}$ is
verified and recorded to the distributed ledger. At the same time, there might
be many available models on the marketplace.

Step 3 (Model Seller download train-required models): The potential seller
$S_{i}$ can see the list of available models on the marketplace and choose to
download a copy of one or multiple models of interest to train with its local
data of $S_{i}$.

Step 4 (Local Model Training): After downloading the models from the
distributed ledger, the sellers train the model based on their local data. The
device, i.e., the seller $S_{i}$, has its own local dataset $D_{i}$. Local
model training aims to minimize the loss function
$f(\mbox{\bf{M}}_{i},S_{i})$, where $\mbox{\bf{M}}_{i}$ is the local model of
device $d_{i}$ and $D_{i}$ is its local dataset.

Step 5 (Local trained model is updated to the ledger): Next, the device
$S_{i}$ is randomly associated with the miner $MI_{i}$ to which it uploads the
trained local model to the distributed ledger via smart contract. The smart
contract has functions to record the updated local models from clients via DLT
interface, e.g., Web3.

Step 6 (Cross-verification of the local models): After receiving the local
model published by IoT device in the format of transactions, the DLT miner
$MI_{i}$ put the local model in newly generated blocks and broadcasts the
model to other DLT miners in the network. Next, until other DLT miners receive
the broadcasted blocks, including the local models of clients, they will
verify the accuracy of local models and put the models to the new generated
blocks. During this process, all the aggregated models are broadcasted to the
all DLT miners in the system, and DLT miners will compare the consistency and
accuracy among aggregated models. To that end, the most one will be chosen as
the correct global model. Then, DLTs miners record the correct global model
and the contribution of the IoT devices into the distributed ledger via smart
contracts features. Otherwise, the rest of global models are considered as
faulty updates.

Step 7 (The generation and propagation of blocks): In order to generate a new
block in the distributed ledger, DLT miners need to compute a block hash for
mining and solve a cryptographic puzzle based on SHA-256, which is a one-way
hash function. As defined in popular Proof-of-Work (PoW) Blockchain, e.g.,
Bitcoin, Ethereum, DLT miners perform a PoW algorithm until it finds a desired
nonce value or receives a new generated block from other DLT miners[26]. There
is a case, however, that the $MI_{i}\in MI$ acts as the DLT miner that finds
the needed nonce value at the earliest, and its candidate block is generated
as a new block and propagated to the other DLT miners in the network.
Meanwhile, the chain can be engaged in forked problem in which multiple DLT
miners find out a nonce value at the same moment. To address this issue, we
use an ACK message that allows DLT miners to transmit only when each DLT miner
gets the new block, which determines whether there is a fork on the main
chain. Then, the DLT miner $MI_{0}$, which creates that newly generated block,
will wait for a waiting time defined by the block ACK. Otherwise, if a fork is
generated again, the process back to to previous phase to resolve the issue.

Step 8 (Settlement): After the model accuracy achieves a particular value in
the smart contract, the smart contract settles the deal between buyer and
sellers. The finalized model is updated to the buyer and the incentive is
funded to the sellers.

Step 9 (Incentive to Sellers): Based on the contribution of each seller, the
smart contract computes their contribution and transfer to sellers appropriate
funds. The smart contract provides a mechanism of transparent and immutable
recording and accounting contribution logs on the distributed ledger. Based on
the contribution history from ledger, the clients can receive the incentives
and rewards in tokens via off-chain payment channels.

Step 10 (Record receipt to the Ledger): All bills and receipts are recorded
immutably in the distributed ledger, which allows participants to check and
control their deal. Besides, we also implemented the off-chain storage
solution named IPFS to store hashes of data locations on the ledger instead of
raw data files. The hashes can be used to query the exact file or models
through the DLT systems.

###  III-C Distributed Shapley Value (DSV) Calculation

Input: Local mini-batch size B, number of participants $m$ per interaction,
$T$ is number of global interactions, number of local epochs $E$, and learning
rate $\eta$.

Output: : $\mathcal{M}^{T}$

1. Initialize $\mathcal{M}_{S}^{0}$, where $S\subseteq\mathcal{N}=\\{1,2,\ldots,n\\}$.

2

3for  _each subset $\mathcal{S}\subseteq\mathcal{N}$ _ do

4 for  _ each round $t=\\{0,1,2,\ldots,T-1\\}$ _ do

5 for  _ each client $i\in\mathcal{S}=\\{1,2,3,\ldots,n\\}$_ do

6 Transmit $\mathcal{M}^{t}$ to $n$ clients;

7 $\mathcal{M}_{i}^{t}\leftarrow ModelUpdate(i,\mathcal{M}^{t})$;

8
$\delta_{\mathcal{S},i}^{(t+1)}\leftarrow\mathcal{M}_{\mathcal{S},i}^{t}-\mathcal{M}^{t}$;

9

10
$\mathcal{M}_{\mathcal{S}}^{t+1}\leftarrow\mathcal{M}_{\mathcal{S}}^{t}+\sum_{i=1}^{n}\frac{|D_{i}|}{\sum_{i=1}^{n}|D_{i}|}\cdot\delta_{\mathcal{S},i}^{t+1}$;

11

12 for  _ $i\in\mathcal{S}=\\{1,2,3,\ldots,n\\}$ _ do

13
$\phi_{i}=\frac{1}{n}\sum_{\mathcal{S}\subseteq\mathcal{N}\setminus\\{i\\}}\frac{U(\mathcal{M}_{\mathcal{S}\cup\\{i\\}}^{t})-U(\mathcal{M}^{t}_{\mathcal{S}})}{\binom{n-1}{|\mathcal{S}|}}$;

14

15

16

17

18Return $\mathcal{M}^{T}$, and $\phi_{1},\phi_{2},\ldots,\phi_{n}$.

19 ModelUpdate ($i,\mathcal{M}$):

20 for  _local epoch $e=\\{1,2,3,\ldots,E\\}$ _ do

21 for  _batch $b$_ do

22 $\mathcal{M}\leftarrow\mathcal{M}-\eta\nabla L(\mathcal{M};b)$;

23 Return $\mathcal{M}^{T}$ to central server.

24

25

Algorithm 2 Standard Federated Shapley FedAvg

1

Input: Input: Local minibatch size B, number of participant $m$ per
interaction, $T$ is number of global interactions, number of local epochs $E$,
and learning rate $\eta$.

2

Output: Output: $\mathcal{M}^{T}$, and $\phi_{1},\phi_{2},\ldots,\phi_{n}$.

3

4Initialize $\mathcal{M}^{0},\widetilde{\mathcal{M}}^{0}_{\mathcal{S}}$, where
$\mathcal{S}\subseteq\mathcal{N}=\\{1,2,3\ldots,N\\}.$

5

6for  _each round $t=\\{0,1,2,\ldots,T\\}$ _ do

7 Transmit $\mathcal{M}^{t}$ to $i\in\mathcal{S}$ clients;

8 $\mathcal{M}_{i}^{t}\leftarrow ModelUpdate(i,\mathcal{M}^{t})$;

9 $\delta_{i}^{t+1}\leftarrow\mathcal{M}_{i}^{t}-\mathcal{M}^{t}$ , $\forall
i\in\mathcal{N}$;

10
$\mathcal{M}^{t+1}\leftarrow\mathcal{M}^{t}+\sum_{i=1}^{n}\frac{|D_{i}|}{\sum_{i=1}^{n}|D_{i}|}\cdot\delta_{i}^{t+1}$;

11 for  _each $\mathcal{S}\subseteq\mathcal{N}$ _ do

12
$\widetilde{\mathcal{M}}^{t+1}_{\mathcal{S}}\leftarrow\widetilde{\mathcal{M}}^{t}_{\mathcal{S}}+\sum_{i\in\mathcal{S}}\frac{|D_{i}|}{\sum_{i\in
S}|D_{i}|}\cdot\delta_{i}^{t+1}$;

13

14

15

16Initialize $m=0$.

17 while  _ Convergence criteria not meet _ do

18 $m=m+1$;

19 $\pi^{m}$: random permutation of clients with data samples to
collaboratively train $\mathcal{M}^{T}$;

20 $v_{0}^{m}\leftarrow U(\widetilde{\mathcal{M}}^{0}_{\emptyset})$;

21

22 for  _$n\in\\{1,2,\dots,|\mathcal{S}|\\}$_ do

23 if  _$|U(\mathcal{M}_{|\mathcal{S}|})-v_{n-1}^{m}| <$ PT _ then

24 $v_{n}^{m}=v_{n-1}^{m}$;

25 else

26 $\mathcal{S}\leftarrow\\{\pi^{m}[1],\pi^{m}[2],\ldots,\pi^{m}[n]\\}$;
$\mathcal{\mathcal{M}}_{\mathcal{S}}^{T}\leftarrow\sum_{i\in\mathcal{S}}\frac{|D_{i}|}{\sum_{i\in\mathcal{S}}|D_{i}|}\cdot\mathcal{\widetilde{\mathcal{M}}}_{i}^{T}$;
$v_{n}^{m}\leftarrow U(\mathcal{\mathcal{M}}^{T}_{\mathcal{S}})$;

27
$\phi_{\pi^{m}[n]}\leftarrow\frac{m-1}{m}\phi_{\pi^{m-1}[n]}+\frac{1}{m}(v_{n}^{m}-v_{n-1}^{m})$;

28

29

30Return $\mathcal{M}^{T}$, and $\phi_{1},\phi_{2},\ldots,\phi_{n}$.

31

32ModelUpdate ($i,\mathcal{M}$):

33 for  _local epoch $e=\\{1,2,3,\ldots,E\\}$ _ do

34 for  _batch $b$_ do

35 $\mathcal{M}\leftarrow\mathcal{M}-\eta\nabla L(\mathcal{M};b)$;

36 Return M to ledger.

37

38

Algorithm 3 AFS Algorithm

The Standard Federated Shapley Value (SFSV) calculates the SV of data
contributors based on equation (2). SFSV trains federated models based on the
different subsets $S$ of contributors, and these models are evaluated on the
standard test set. However, computing the SV directly according to SFSV is
time-consuming because models on all the combinations of data sets need to be
trained and evaluated. By default, the SV computes the average contribution of
a data source to every possible subset of other data sources. So that,
evaluating the SV incurs significant communication and computation cost when
the data is decentralized [27]. Consequently, for data SV in the FL
environment, the methods in [28, 25] to calculate SV introduce extra training
rounds on combinations of datasets from different data providers. Furthermore,
the cost for extra rounds for training models could be expensive when the data
volume is large. Therefore, there is a need for new strategies to evaluate the
data value in FL.

The main idea to that end is to exploit the gradients information during the
training process of the global model $\mathcal{M}$ to approximately
reconstruct the local models trained with different combinations of the
client’s datasets. Thereby, our approach (as described in Algorithm 3)
eliminates the burden for the local models to be frequently re-trained to
evaluate clients’ contributions. In fact, the SV does not consider the order
of data sources. However, in FL, it is of significant importance to take into
account the order of data used for the model training so as to ensure a fair
convergence. Furthermore, the updates of model are enforced to diminish over
time by using, for example, a decaying learning rate[29]. Hence, the sources
used towards the end of the learning process could be less influential than
those used earlier. Therefore, to accommodate these attributes of learning
properties in the decentralized model training paradigm of FL, we need to
define new and efficient ways to compute SV. In this regard, based on the
neutrality of FL, the SV for FL (FSV) could be computed in two different
strategies.

The first method (called Single-Cal) reconstructs models by updating the
initial global model $\mathcal{M}$ in FL with the gradients in different
rounds and calculates the FSV by the performance of these reconstructed
models. For example, if we want to reconstruct the model of
$\mathcal{M}_{(i,j)}$ trained on the datasets of $D_{i}$ and $D_{j}$ of
corresponding users, use the gradients information from sellers $i$ and $j$ in
each round to update the initial global model $\mathcal{M}$ generated by the
buyer. Then, the contribution is calculated using equation (2).

The second method (called Multi-Cal) calculates FSV in each training round by
updating the global model $\mathcal{M}$ from the previous training round with
the value of gradients in the current training round. Next, the FSVs are
aggregated from multiple rounds to get the final result. Therefore, there is
no extra training process needed; these methods are considered efficient. The
main difference between these two strategies is that the first method
approximates models through complete global iterations and only evaluates them
to find SV afterwards. The second one approximates and evaluates models for
every global iteration and calculates the marginal contribution for each
global iteration. So that makes the second method more computationally
expensive than the first one. To address this issue, we propose a new
algorithm AFS based on the first approach with the use of Truncated Monte-
Carlo (TMC) [25] in Algorithm 3. In principle, AFS is an engineered derivative
of the TMC algorithm to characterize clients’ contributions with their
available data samples in the collaborative training framework. In doing so,
AFS evaluates the marginal contribution of each client instead of a subset of
training data points, unlike the TMC method; thus, allowing clients to engage
in the ML model trading with the offered incentive signals based on their data
contributions.

Specifically, the first part of the algorithm shows the operation of the
distributed ledger, lines 1–8 and lines 10–19. In line 1, the global model
$\mathcal{M}^{0}$ and reconstructed models based on different chosen subsets
$\mathcal{S}\subseteq\mathcal{N}=\\{1,2,\ldots,N\\}$ are initialized. Next,
the distributed ledgers broadcast the global model $\mathcal{M}^{t}$ to $n$
selected clients in each global training round $t$ in line 3, and then receive
the updates $\mathcal{M}^{t}_{i}$ from these clients in line 4 and the
gradients of clients $\delta^{t}_{i},\forall i\in\mathcal{S}$ for model
aggregation are computed. After that, the global model is updated in line 6 as

$\mathcal{M}^{t+1}\leftarrow\mathcal{M}^{t}+\sum_{i=1}^{n}\frac{|D_{i}|}{\sum_{i=1}^{n}|D_{i}|}\cdot\delta_{i}^{t+1}.$
(3)

Next, instead of updating all the local models $\mathcal{M}^{t}_{i},\forall
i\in\mathcal{S}$ in every global interactions $t=\\{0,1,\ldots,T\\}$, we can
update only $n$ models $\widetilde{\mathcal{M}}^{t}_{i}$ and compute
$\mathcal{M}^{T}_{\mathcal{S}}$ directly as a weighted average at the end, as
$\widetilde{\mathcal{M}}^{t+1}_{i}\leftarrow\widetilde{\mathcal{M}}^{t}_{i}+\frac{|D_{i}}{\sum_{i=1}^{n}|D_{i}|}\delta_{i}^{t+1}$
.

We observe that evaluation of a model incurs a considerable cost in terms of
time, especially if the test set is large. And with the basic idea of a
single-round algorithm, we must reconstruct and evaluate $2^{n}$ models.
Hence, we applied the method of TMC to decrease the computation cost and
developed a tailored variant of TMC, i.e., the AFS algorithm, to address this
issue. The details of the adapted TMC method is as follows. First, we sample a
random permutation of clients $\pi^{m}$ with their data samples used to train
the global model [28]. After that, we scan from the first clients to the last
client and calculate the marginal contribution of every new client’s data in
the training process. By repeating this process over multiple permutations,
the approximation of SV is the average of all the calculated marginal
contributions. The while loop is run until certain convergence criteria are
met. In this work, we stop the loop when the average percentage change after a
TMC iteration $m$ is less than a certain threshold.

In line 21, the trained federated model $\mathcal{M}^{T}$ and the SVs are
finally obtained. The local training part for the clients (lines 22–25) show
how the clients use private data to train the model received from the
distributed ledger. The clients use the classical gradient descent algorithm
and report their updated local models
$\mbox{\bf{M}}_{i|i=\\{1,2,3,\ldots,n\\}}$ to the distributed ledger.

###  III-D Performance bound on AFS algorithm

An analytical bound on the AFS algorithm can be derived by taking the
properties of TMC sampling approach into account [25]. We consider AFS
estimates the contribution of the individual client in the federated setting
for a supervised learning task with probability at least (1-$\alpha$) that our
estimator error is $\epsilon$. Then, we are interested in evaluating the
general performance bound on AFS such that

$\textrm{Pr}(|\phi^{F}-\phi^{S}|\geq\epsilon)\leq\alpha,$ (4)

where $\phi^{S}=<\phi^{S}_{1},\phi^{S}_{2},\ldots,\phi^{S}_{n}>$ is the vector
of Shapley contributions generated by the standard SV and
$\phi^{F}=<\phi^{F}_{1},\phi^{F}_{2},\ldots,\phi^{F}_{n}>$ is the
approximation FSV using the proposed AFS method. Assume that we know the data
distribution of clients to evaluate its marginal contribution. Then, the
sampling $|\mathcal{S}|$ made during the evaluation process reflects the bound
on the obtained approximation of AFS. Without loss of generality, we assume it
is possible to quantify the range $v$ of the client’s data marginal
contribution in improving the global model. Then, we have the following Lemma
1.

######  Lemma 1.

Considering the TMC sampling approach [25] for evaluating the Shapley value,
we have a minimum permutation $|\mathcal{S}|$ for a known range of the
client’s marginal contribution $v$ defining an upper bound of
$\mathcal{O}\bigg{(}\frac{v}{|\mathcal{S}|}\bigg{)}$ on AFS such that
$\alpha\geq 2\exp{\bigg{(}\frac{-2|\mathcal{S}|\epsilon^{2}}{v^{2}}\bigg{)}}$
satisfies for $0\leq\epsilon,\alpha\leq 1$.

###### Proof.

The proof can be derived using Hoeffding theorem [30] for a known range of
marginal contribution of clients. In practice, the distributed ledger can
reuse the average of marginal contributions of clients, with known range $v$,
to derive a sampling permutation $|\mathcal{S}|$. Then, we have
$\textrm{Pr}\bigg{(}\sum\nolimits_{i\in\mathcal{S}\subseteq
N}(\phi_{i}-\mathbb{E}(\phi_{i}))\geq\Delta\bigg{)}\leq
2\exp{\bigg{(}\frac{-2|\mathcal{S}|\Delta^{2}}{v^{2}}\bigg{)}}$. Taking the
average of marginal contributions on the left-hand side of the inequality, and
combining it with (4), we get $\text{Pr}(|\phi^{F}-\phi^{S}|\geq\epsilon)\leq
2\exp{\bigg{(}\frac{-2|\mathcal{S}|\epsilon^{2}}{v^{2}}\bigg{)}}$. This
concludes the proof. ∎

Figure 5: Performance analysis of AFS algorithm.

In Fig. 5, we show the results on performance analysis of AFS algorithm. We
observed variability in the minimum permutation $|\mathcal{S}|$ required to
ensure a defined deviation between the average value of contributions across
clients, as measured using AFS, and the standard SV. For tighter bounds, the
number of required permutations is large. This is intuitive, as the
distributed ledger expects a larger sampling value $|\mathcal{S}|$ to define
better confidence bound on the performance that minimizes the approximation
error using AFS.

##  IV Performance Evaluation

###  IV-A Experimental Settings

To demonstrate the applicability of our proposed system, we implement a proof-
of-concept for the trading model in an IoT network. In this section, we
introduce enabling technologies involved with the prototype.

####  IV-A1 Distributed Ledger

In this study, we implement Ethereum platform for the experimental.
Ethereum444https://ethereum.org/ is a distributed public blockchain network
that focuses on running programming code of any decentralized application.
Specifically, Ethereum is a platform for sharing information across the globe
that cannot be manipulated or changed. Ethereum has its own cryptocurrency,
called Ether (ETH), and its own programming language, called Solidity. The
decentralized applications on the network is called Đapps. Practically,
Ethereum provides a convenient platform for development and smart contracts
system to integrate with FL. We run Ethereum network via
Ganache555https://www.trufflesuite.com/ganache which is a personal blockchain
for rapid Ethereum distributed application development.

####  IV-A2 Datasets

In the scope of this study, we conducted the experiments on the MNIST data
set. The dataset contains around 60,000 training images and over 10,000
testing images. Each client holds a part of dataset locally depending on the
scenarios.

####  IV-A3 IoT Devices and Workstation

We use Raspberry Pi 3 with the following configurations: Pytorch, OS Raspbian
GNU/Linux 10, and Python version 3.7. We note that CUDA is not available for
the model. The workstation has the system configurations as CPU i7-7700HQ, GPU
GTX, Pytorch, OS Linux Ubuntu 20.04 , Python version 3.7.8 using Anaconda, and
the CUDA version 11. These IoT devices are connected via Wifi access point.

Figure 6: Blockchain-enabled model trading testbed. The testbed includes DLT
Ethereum network running over Ganache, IPFS storage to address scalability
issue, monitor dashboard and 5 IoT raspberry devices standing for marketplace
participants as well as DLT clients.

####  IV-A4 Evaluation Metrics.

We consider several performance metrics for comparison.

  * •

Cost of Smart Contract: We study the fees made by users to compensate for the
computing energy required to process and validate transactions on the
Ethereum.

  * •

Incentive per worker: The amount of tokens delivered to sellers based on their
contributions of training the model.

  * •

Maximum Different: The performance score function $U$ is chosen to be the
accuracy function. The SVs are then calculated according to the different
schemes. For comparison of the accuracy of the SV, all SVs calculated are
first standardized by scaling them by a common factor such that
$\sum_{i=1}^{n}\phi_{i}=1$.

This is appropriate because profit distribution will likely be based on the
percentage contribution. Then, the maximum different $D_{max}$ measures the
maximum difference that a data provider should be allocated by the definition
and by approximated calculation. The calculation is shown as below:

${\color[rgb]{0,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,0}\pgfsys@color@gray@stroke{0}\pgfsys@color@gray@fill{0}D_{\max}=\max_{i\in\\{1,\ldots,n\\}}|\phi_{i}^{F}-\phi_{i}^{S}|}.$
(5)

####  IV-A5 Scenarios

  * •

(S1). In scenario 1, the compared algorithms have same distribution with same
dataset size, i.e., each client dataset $D_{i},\forall i\in\mathcal{N}$ has
the same amount of training image samples.

  * •

(S2). In scenario 2, we introduce the case with same distribution but
different dataset size. The training set is divided randomly into 5 parts with
the same ratio of data size.

  * •

(S3). In scenario 3, we use different distribution with same dataset size.
Each client’s dataset $D_{i},\forall i\in\\{1,2,3,4,5\\}$ has the same size,
but the training images are not equally divided for each digit.

  * •

(S4). In scenario 4, we consider the case having an added noise feature with
same dataset. First, we split the training set in a similar manner as (S1).
Afterwards, we generate Gaussian noise for the dataset. This is done by
adjusting the standard deviation of the normal distribution.

TABLE II: EXECUTION COSTS OF SMART CONTRACTS

Smart Contracts |  From |  Gas |  Ether |  USD
---|---|---|---|---
Contract Registry |  0x283D382F |  1459430 |  15.9$\cdot 10^{-5}$ |  0.0723
AddWorker |  0x283D382F |  452467 |  45.2$\cdot 10^{-5}$ |  0.0692
AddWorker |  0x283D382F |  452545 |  45.2$\cdot 10^{-5}$ |  0.0692
AddWorker |  0x283D382F |  452436 |  45.2$\cdot 10^{-5}$ |  0.0692
AddWorker |  0x283D382F |  452545 |  45.2$\cdot 10^{-5}$ |  0.0692
AddWorker |  0x283D382F |  452436 |  45.2$\cdot 10^{-5}$ |  0.0692
ModelTransmission |  0x5846F427 |  19374 |  19.3$\cdot 10^{-5}$ |  0.1621
ModelTransmission |  0x9dD8Fd06 |  243482 |  24.3$\cdot 10^{-5}$ |  0.0902
ModelTransmission |  0x98HF8F94 |  228779 |  22.3$\cdot 10^{-5}$ |  0.1121
ModelTransmission |  0x8H9FH780 |  253924 |  25.3$\cdot 10^{-5}$ |  0.0951
ModelTransmission |  0x0932FD99 |  263924 |  19.3$\cdot 10^{-5}$ |  0.0571
ModelTraining |  0x5846F427 |  223924 |  22.3$\cdot 10^{-5}$ |  0.1021
ModelTraining |  0x9DD8Fd06 |  253924 |  25.3$\cdot 10^{-5}$ |  0.0951
ModelTraining |  0x98HF8F94 |  193924 |  19.3$\cdot 10^{-5}$ |  0.0571
ModelTraining |  0x8H9FH780 |  253924 |  25.3$\cdot 10^{-5}$ |  0.0951
ModelTraining |  0x0932FD99 |  253924 |  19.3$\cdot 10^{-5}$ |  0.0571
ModelAggregation |  0x5846F427 |  324942 |  32.4$\cdot 10^{-5}$ |  0.0766
ModelAggregation |  0x9dD8Fd06 |  283445 |  22.4$\cdot 10^{-5}$ |  0.0408
ModelAggregation |  0x98HF8F94 |  214939 |  21.4$\cdot 10^{-5}$ |  0.0709
ModelAggregation |  0x8H9FH780 |  253924 |  25.3$\cdot 10^{-5}$ |  0.0951
ModelAggregation |  0x0932FD99 |  193924 |  19.3$\cdot 10^{-5}$ |  0.0571
Settlement |  0x283D382F |  212559 |  21.3$\cdot 10^{-5}$ |  0.0712
PayChannelExecute |  0x283D382F |  212538 |  21.2$\cdot 10^{-5}$ |  0.0702

  * •

* 1 Ether = $10^{9}$ Gwei; 1 USD = 246,940.5627 Gwei

###  IV-B Results

(a) Received incentive per client with the dataset distribution ratio
2:2:2:2:2.

(b) Received incentive per client with the dataset distribution ratio
3:3:2:1:1.

Figure 7: Incentive of clients.

####  IV-B1 Smart Contract Execution Cost

In this part, the proof-of-concept of proposed model trading platform is
deployed in a private Ethereum Blockchain called
Ganache666https://www.trufflesuite.com/ganache. In distributed application
Dapps, the smart contract plays as key role in controlling and autonomously
executing pre-defined agreements between the participants. We implemented and
tested smart contracts using Remix IDE777https://remix.ethereum.org/. In
Ethereum network, there is a fee called _gas_ , needed to pay for any
operation or transaction execution that changes the DLT states, which
guarantees that smart contracts running in Ethereum Virtual Machine (EVM)[24]
will be terminated eventually. In the scope of this research, we used
Gwei888https://www.cryps.info/ to evaluate the cost of different operations,
for example, _AddWorker, ModelTransmisson, ModelTraning,_ or _Settlement_ in
the model trading process. The result is demonstrated in Table. II.

Figure 8: Shapley Value of each seller in different methods Figure 9:
Comparison of execution time and $D_{\max}$ between algorithms.

####  IV-B2 Incentive per client

In Fig. 7, we show the comparison of received incentives by each training
client based on their contribution to the global model training. The incentive
is equivalent to tokens clients receive. As expected, in Fig. 7(a), where the
MNIST dataset is divided equally with a ratio of 2:2:2:2:2 for five involved
clients, the amount of tokens they receive are almost similar as expected. In
Fig. 7(b), we show the comparison of the received percentage of tokens that
clients can achieve with the dataset ratio of 3:3:2:1:1. We observe that
client 1 and client 2 has the same amount of dataset, so they receive the same
amount of tokens for their contribution, similar to the case for clients 4 and
5. Note that the sellers can train the models with poor quality, which, in
fact, reduces the stability and performance of the global models. In this
regard, there exist several mechanisms to handle such dishonest reporting of
parameters in the FL setting, such as [31, 13, 32]. Similar to this, the DLT
keeps track of the contribution of devices and the gradient information and
the size of data samples to regularly infer (check) the relationship between
the expected model quality, reported data samples, and the obtained SV as Fig.
8; hence, dealing untruthful reporting. However, the detailed study of this
mechanism is out of scope for this work. In Fig. 8, the _AFS_ shows a better
performance while other methods turns out quite random SVs, especially in
scenario 2 and 4 where the size of dataset is random and noise added.

####  IV-B3 Execution time and maximum different comparison

In Fig. 9, we show the time performance of exact FL, Single-Cal, Multi-Cal,
and AFS protocol. The Multi-Cal algorithm is more computational expensive than
the Single-Cal algorithm. The standard exact method is the slowest one because
the standard Shapley Value is naturally not compatible with the Federated
Learning. In the scenario 1, each worker has same quality and quantity of
dataset, so that we expect each worker has same contribution and receive
equally the amount of incentive. The results show that the Single-Cal and AFS
algorithm have higher efficiency in execution time. The exact method is around
5 times slower than the rest of methods because of frequent model retrain
process. Meanwhile, the $D_{max}$ of methods are relatively low, around
$0.05$. Similar in scenario 2 with the same size of dataset and different
distribution, AFS and Single-Cal have better performance in running time and
the accuracy. However, in scenario 4 with more noisy data, the Multi-Cal shows
better results, $\approx 10\%$ in run-time but and $\approx 15\%$ in terms of
maximum different value.

##  V Related Works

In this section, we first present the current works on asset trading based on
Blockchain and data valuation.

Blockchain-based asset trading. With the spread of ubiquitous marketplaces, it
became relevant to explore the application of IoT data trading in marketplace
environments. For instance, the authors in [33] considered a dynamic
decentralized marketplace and introduced the architecture for trading IoT data
accordingly. The approach involves a 3-tier method is used: 1) data provider,
2) broker and 3) data consumer. The primary purpose of DLTs in their function
is to manage the conditions of agreements between the parties involved. In
addition, the design has a reputation system that penalizes members and lowers
their rating. The authors in [34] invested the optimization problem of revenue
maximization with envy-free guarantee. The authors studied two scenarios
including unit demand consumers and single minded consumers, and showed the
optimization problem is APX-hard for both scenarios, which can be efficiently
addressed by a logarithmic approximation. The authors in [35] took into
account the trading of IoT streaming data with the presented marketplace
model, where fraudulent activity during data exchange is limited. To do so,
the authors introduced periodic checkpoints during data trading. In [36], the
authors proposed another marketplace which flows of IoT data are the main
digital assets exchanged utilizing Oracles for the off-chain queries.The
authors in [31] presented a trading mode based on smart contracts. In
particular, the authors employ arbitration that handles disputes during the
data trading, particularly, over the data availability, and incorporates AI/ML
to ensure fairness during data exchange.

Data Valuation. Evaluating the value of data has been received significant
attention from both academia and industrial areas. Several works studied data
valuation strategies and their applications. In this regard, the authors in
[37] defined the data valuation in several categories, such as: (i) query-
based pricing, where prices are attached to user-initiated queries [21], [38],
[39], (ii) data attribute-based pricing, where the price model considers data
attributes, such as the age of data and its credibility, using the mechanism
of public price registries [40], and (iii) auction-based pricing, where the
price is dynamically set following auction mechanisms [41, 42]. In
[25],multiple approximation strategies for optimizing the computation complex
of SV for training data are introduced. Besides, the authors proposed an
soltiion to compute exact SC in specific scenario, e.g nearest neighbor
classifiers. Besides, the SV also is applied in various AI/ML application, for
example, to measure the importance of model features[43, 44]. In specific, the
authors addressed the problem when the same data points get the same values,
and relationship between data distributions and SV function. In addition, the
authorsproposed an idea of distributional SV occurs resemblance to the Aumann-
SV[45]. In practical manner, the authors in [46] proved that the performance
of model training can be improved by removing the data with low SV value. In
contrast, the performance will be decreased if we deleting the training data
with high SV values.

##  VI Conclusion

In this paper, we proposed a DLT-based marketplace for trading ML models,
which helps companies and organizations train their learning models in a
scalable and efficient manner. An incentive mechanism exists to stimulate
participants in joining and training the learning models on the marketplace,
which pays participants based on their contributions to train the model. To
that end, an extended Data Shapley Value (DSV) for the federated environment
is proposed to measure each participant’s contribution in the model training
process. Finally, with extensive experimental evaluations with Ethereum
Blockchain to build a marketplace for model trading using smart contracts and
IoT devices acting as participants, we demonstrated the design and performance
of the proposed ecosystem.

##  VII Acknowledgment

This work has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No. 957218 (Project IntellIoT).

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|  Lam Duc Nguyen (S’20) is a Ph.D. Fellow at Aalborg University. He obtained
Master Degree in Computer Science at Seoul National University, and a Bachelor
in Telecommunication at Hanoi University of Science and Technologies in 2019
and 2015, respectively. His research includes Distributed Systems, Blockchain,
Smart Contracts, the Internet of Things, and applying Blockchain and Federated
Learning to enhance the efficiency of Blockchain-based IoT monitoring
Networks. He receives Outstanding Paper Award for the research about scaling
Blockchain in Massive IoT at the IEEE World Forum Internet of Things 2020,
travel grant from Linux Foundation 2020, Best Research Award for a solution of
Blockchain-based CO2 Emission Trading from VEHITS 2021. He is Hyperledger
Member, IEEE Student Member, and IEEE ComSoc Student Member.
---|---
|  Shashi Raj Pandey (M’21) is currently working as a Postdoctoral Researcher
at the Connectivity Section, Aalborg University. He received the B.E. degree
in Electrical and Electronics with a specialization in Communication from
Kathmandu University, Nepal in 2013, and the Ph.D. degree in Computer Science
and Engineering from Kyung Hee University, Seoul, South Korea, in August,
2021. He served as a Network Engineer at Huawei Technologies Nepal Co. Pvt.
Ltd, Nepal from 2013 to 2016. His research interests include network
economics, game theory, wireless communications, data markets and distributed
machine learning.
---|---
|  Beatriz Soret [M’11] received her M.Sc. and Ph.D. degrees in
Telecommunications from the Universidad de Malaga, Spain, in 2002 and 2010,
respectively. She is currently an associate professor at the Department of
Electronic Systems, Aalborg University, and a Senior Research Fellow at the
Communications Engineering Department, University of Malaga. Her research
interests include LEO satellite communications, distributed and intelligent
IoT, timing in communications, and 5G and post-5G systems.
---|---
|  Arne Bröring is a Senior Key Expert Research Scientist at Siemens
Technology in Munich. He received his PhD in 2012 from the University of
Twente (Netherlands). Dr. Bröring has contributed to over 90 publications in
the field of distributed systems and has served on various program committees
and editorial boards. His research interests range from distributed system
designs, over sensor networks, and Semantic Web, to the Internet of Things. At
Siemens, he has been in charge of the technical & scientific coordination of
large EU research projects (BIG IoT and IntellIoT). Before joining Siemens,
Dr. Bröring worked for the Environmental Systems Research Institute in Zurich,
the 52°North Open Source Initiative, and led the Sensor Web and Simulation Lab
at the University of Münster.
---|---
|  Petar Popovski (Fellow, IEEE) is a Professor at Aalborg University, where
he heads the section on Connectivity and a Visiting Excellence Chair at the
University of Bremen. He received his Dipl.-Ing and M. Sc. degrees in
communication engineering from the University of Sts. Cyril and Methodius in
Skopje and the Ph.D. degree from Aalborg University in 2005. He is a Fellow of
the IEEE. He received an ERC Consolidator Grant (2015), the Danish Elite
Researcher award (2016), IEEE Fred W. Ellersick prize (2016), IEEE Stephen O.
Rice prize (2018), Technical Achievement Award from the IEEE Technical
Committee on Smart Grid Communications (2019), the Danish Telecommunication
Prize (2020) and Villum Investigator Grant (2021). He is a Member at Large at
the Board of Governors in IEEE Communication Society, Vice-Chair of the IEEE
Communication Theory Technical Committee and IEEE TRANSACTIONS ON GREEN
COMMUNICATIONS AND NETWORKING. He is currently an Area Editor of the IEEE
TRANSACTIONS ON WIRELESS COMMUNICATIONS and, from 2022, an Editor-in-Chief of
IEEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS. Prof. Popovski was the
General Chair for IEEE SmartGridComm 2018 and IEEE Communication Theory
Workshop 2019. His research interests are in the area of wireless
communication and communication theory. He authored the book “Wireless
Connectivity: An Intuitive and Fundamental Guide”, published by Wiley in 2020.
---|---