∎

11institutetext:  11footnotemark: 1 Corresponding author.
44footnotemark: 4 Equal contribution.
55footnotemark: 5 Hithink RoyalFlush Information Network Co. Ltd., Hangzhou,
China.
66footnotemark: 6 Information Science and Electrical Engineering Department,
Kyushu University, Fukuoka, Japan.
77footnotemark: 7 Baidu Inc., Beijing, China.
88footnotemark: 8 Unicom Digital Tech., Beijing, China.
99footnotemark: 9 Boston Consulting Group, Beijing, China.

# Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey
on Blockchain-based Federated Learning

Ji Liu11footnotemark: 144footnotemark: 455footnotemark: 5 Chunlu
Chen44footnotemark: 466footnotemark: 6 Yu Li77footnotemark: 7 Lin
Sun88footnotemark: 8 Yulun Song88footnotemark: 8 Jingbo Zhou77footnotemark: 7
Bo Jing77footnotemark: 7 Dejing Dou99footnotemark: 9

(Accepted: March 28, 2024)

###### Abstract

While centralized servers pose a risk of being a single point of failure,
decentralized approaches like blockchain offer a compelling solution by
implementing a consensus mechanism among multiple entities. Merging
distributed computing with cryptographic techniques, decentralized
technologies introduce a novel computing paradigm. Blockchain ensures secure,
transparent, and tamper-proof data management by validating and recording
transactions via consensus across network nodes. Federated Learning (FL), as a
distributed machine learning framework, enables participants to
collaboratively train models while safeguarding data privacy by avoiding
direct raw data exchange. Despite the growing interest in decentralized
methods, their application in FL remains underexplored. This paper presents a
thorough investigation into Blockchain-based FL (BCFL), spotlighting the
synergy between blockchain’s security features and FL’s privacy-preserving
model training capabilities. First, we present the taxonomy of BCFL from three
aspects, including decentralized, separate networks, and reputation-based
architectures. Then, we summarize the general architecture of BCFL systems,
providing a comprehensive perspective on FL architectures informed by
blockchain. Afterward, we analyze the application of BCFL in healthcare, IoT,
and other privacy-sensitive areas. Finally, we identify future research
directions of BCFL.

###### Keywords:

Federated learning Decentralization Blockchain Security Privacy

8cm(3cm,15cm) To appear in KAIS

##  1 Introduction

Blockchain is an innovative technology, which fundamentally reshapes
transactions and interplays with various entities such as institutions and
governments, and verifies authenticity processes. Initially devised for the
digital currency Bitcoin, the capabilities of blockchain transcend its initial
purpose, providing support for a diverse range of applications, from Peer-to-
Peer (P2P) payment services to the management of supply chains. Essentially, a
blockchain functions like a traditional ledger, documenting transactions that
involve the transfer of money, goods, or secure data. Its structure, making it
virtually impossible to alter data without detection by other users, enhances
its security. This characteristic shifts verification systems from centralized
to decentralized, where the consensus of multiple users facilitates the
validation process.

The mechanics of blockchain involve the aggregation and organization of data
into blocks, subsequently fortifying these blocks through cryptography. At the
core of blockchain security lies a hash function, a cryptographic algorithm
that solidifies the connections between blocks. This hash function generates a
unique character string for each block, intricately interwoven into the
succeeding block, forming a secure chain. Any endeavors to tamper with a
previously established block disrupt this chain of hashes, resulting in a
mismatch and exposing the attempted modification. The adaptability of
blockchain extends beyond tracking commercial transactions; it can effectively
store and safeguard sensitive information. Despite its vast potential, this
technology is still in its infancy and must overcome various obstacles before
achieving widespread adoption. Nevertheless, blockchain signifies a
revolutionary shift in how entities and individuals engage, presenting a
straightforward and secure mechanism to establish trust for virtually any
transaction type.

Federated Learning (FL) has been a very hot topic in recent years. First, as
people pay more attention to data privacy, more and more users are unwilling
to share their private data. In addition, various countries have also
introduced corresponding laws and regulations to restrict the behavior of data
collectors, such as the Cybersecurity Law of the People’s Republic (CLPR) of
China CCL , the General Data Protection Regulation (GDPR) GDPR , the
California Consumer Privacy Act (CCPA) CCPA , and the Consumer Privacy Bill of
Rights (CPBR) Gaff2014 , Internet companies need to be further responsible for
user data.FL is a machine learning technique in which people train algorithms
on multiple distributed edge devices or servers with local data samples. This
approach differs significantly from traditional centralized machine learning
techniques, which upload all local datasets to a single server, while more
classic decentralized approaches typically assume that the local data samples
are all the same Distribution. The emergence of FL protects the privacy of
user data to a certain extent, and achieves the effect of “available and
invisible” liu2022distributed .

The fusion of blockchain and FL amalgamates the most advantageous features of
both technologies, yielding a resilient and efficient system characterized by
heightened data privacy and security. The decentralized nature of blockchain
ensures equitable rights for all network nodes, mitigating the risks
associated with centralized systems and protecting against data breaches. Its
immutable and traceable nature provides inherent data integrity, deterring
malicious manipulation and fostering trust among participants. Integrating
blockchain into FL promotes a secure and privacy-preserving distributed
learning environment, where sensitive data remains on edge devices while
ensuring system security and stability. This symbiotic relationship between
blockchain and FL paves the way for the development of robust and reliable
decentralized machine learning systems, with broad applications in various
domains.

Currently, there are numerous researchers exploring BlockChain-based FL (BCFL)
architectures. A comprehensive review berdik2021survey  emphasizes the
potential and complexities of blockchain as a service within today’s
information systems, while highlighting the implications of blockchain in
various industries. The integration of blockchain with cloud and edge
computing paradigms is underscored as being of paramount importance. The
review qammar2023securing  explores blockchain’s potential to enhance FL by
eliminating the need for centralized servers, thus solving problems like
private information disclosure and high communication costs. The concept of
BCFL is dissected in a study li2022blockchain , with a focus on the unique
challenges, structural design, platforms, incentive mechanisms, and
applications it presents. A decentralized approach to FL, involving the use of
Swarm Learning (SL), is meticulously explored in another investigation
han2022demystifying . Here, a permissioned blockchain is introduced to ensure
secure member onboarding and dynamic leader election, thereby facilitating
highly decentralized deep learning. A blockchain-based solution aimed at
enhancing accountability and fairness in FL systems is presented in a
different study lo2021blockchain . This approach includes a smart contract-
based data-model provenance registry and a weighted fair data sampler
algorithm. The introduction of SPDL, a decentralized learning scheme
integrating blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT
Gradients Aggregation Rule (GAR), and differential privacy, is discussed in
another work xu2022spdl . This method ensures efficient machine learning while
safeguarding data privacy, Byzantine fault tolerance, transparency, and
traceability. Lastly, the Blockchain Assisted Decentralized Federated Learning
(BLADE-FL) framework ma2020federated  is brought forward as a fully
decentralized framework, wherein both training and mining responsibilities are
assigned to full nodes. Numerous studies have explored the architectures of
BCFL, shedding light on its potential benefits and inherent challenges.
Diverging from earlier efforts that have investigated distinct aspects of
BCFL, our paper’s value is in amalgamating these diverse methodologies. We aim
to offer a unified, exhaustive analysis of BCFL systems. This synthesis not
only illuminates the current landscape of BCFL but also lays the groundwork
for future advancements in this area. By developing a generic BCFL system
architecture that encompasses essential components such as the infrastructure,
network, communication, algorithms, blockchain consensus, and application
layers, we provide a foundational framework for both evaluating existing FL
systems and guiding the development of forthcoming BCFL solutions. Our
comprehensive approach aims to catalyze innovation within BCFL by addressing
gaps in existing research and proposing new directions for exploration. In
this paper, we conduct an extensive analysis of BCFL systems. The essence of
our work lies in synthesizing existing methodologies and outlining future
research directions. A significant contribution of this research is the
development of a generic BCFL system architecture. This architecture,
structured into key layers including infrastructure, network, communication,
algorithms, blockchain consensus, and application, serves as both an
evaluative tool for existing FL systems and a foundational guide for the
development of future BCFL systems. Moreover, we delve into the diverse
applications of BCFL across various sectors, highlighting its versatility and
potential for innovation. Our study aims to provide a comprehensive
perspective on BCFL systems, while providing the basis for future research in
the field of BCFL systems.

The rest of the manuscript is organized as follows. In Section 2, we introduce
the basic concepts of blockchain and FL and discuss their relationship. In
Section 3, we provide detailed information on the architecture of FL systems,
including the infrastructure layer, network layer, communication layer,
algorithm layer, Blockchain consensus, and application layer. In addition, we
discuss the current state of research and challenges related to each topic.
Finally, section 4 and section 5 discuss the future directions and concludes.

##  2 An Overview of Blockchain and Federated Learning

In this section, we introduce the basic concepts of blockchain and FL. Then,
we discuss the privacy preserving in FL. And the necessity of integrating
blockchain and FL.

###  2.1 Blockchain and Distributed System

Distributed systems are networks of computers or nodes that work together to
form a unified system, characterized by decentralization, consensus, fault
tolerance, and scalability. Decentralization distributes control and decision-
making across various nodes, thereby bolstering fault tolerance, scalability,
and resilience. Consensus mechanisms protect the system by ensuring that all
nodes consistently concur on the state of the system, even under adverse
conditions such as failure or malicious actions. Fault tolerance denotes the
ability of a distributed system to handle the malfunction of an individual
node or component without compromising the functionality of the entire system.
This resilience is achieved through strategies such as redundancy,
replication, and comprehensive error-handling mechanisms. Scalability refers
to the system’s capability to horizontally scale by incorporating additional
nodes to accommodate increasing workloads and expanding user bases.

Figure 1: Blockchain Network and Transaction Blocks.

Blockchain is a special distributed ledger technology that ensures a secure
kolb2020core ; kalodner2020blocksci , immutable record of transactions or
data. Blockchain functions on top of a distributed system and incorporates
principles such as cryptography, transparency and immutability, consensus
mechanisms, smart contracts, and trust and security, as shown in Figure 1.
Blockchain employs cryptography to safeguard the integrity and security of
data, authenticate transactions, and shield information stored on the
blockchain through hash functions, digital signatures, and encryption
liang2020secure . Each transaction recorded on the blockchain is transparent,
visible to all network participants, and virtually indelible once added to the
blockchain - assuring immutability and traceability. Through a consensus
mechanism, blockchain facilitates agreement among distributed nodes on the
validity and sequencing of transactions garay2020sok ; wang2019survey .
Supported by most blockchain platforms, smart contracts are self-executing
agreements with predefined rules inscribed on the blockchain. These smart
contracts autonomously carry out transactions and enforce mutually agreed
terms, thereby negating the need for intermediaries. Blockchain technology
bolsters trust by eliminating central institutions or intermediaries and makes
it challenging for malign actors to tamper with data, courtesy of its
distributed features and cryptographic security measures lugan2019secure ;
chen2018machine . In addition, researchers have focused on improving the
throughput of blockchain systems peng2020falcondb . A method involving the
construction of a blockchain system with multiple subchains is outlined in
yu2020ohie . This system facilitates simultaneous mining operations across all
subchains, thereby ensuring security, liveliness, and high throughput in the
blockchain protocol. In another approach, the Red Belly Blockchain Consensus
(RBBC) is introduced crain2021red . This consensus protocol aims to bolster
security and achieve high throughput, particularly in scenarios involving a
large number of consensus nodes.

The blockchain system is fundamentally a distributed system, and the core
challenges of distributed systems revolve around consistency and consensus.
Within distributed systems, the terms synchronous and asynchronous carry
specific implications. Synchronization entails that each node in the system
has an upper limit on clock error, and message transmission must be completed
within a specified time; otherwise, it is deemed a failure. Simultaneously,
the processing time for each node to handle the message is predetermined. In
synchronous systems, the identification of lost messages is relatively
straightforward. On the other hand, asynchronous signifies that each node in
the system may have a significant clock difference, and the time taken to
process a message at each point can vary arbitrarily, making it challenging to
determine where a message has not received a response. In general, blockchain
technology is mainly divided into two types: permissionless, exemplified by
Bitcoin, and permissioned, with Fabric being a prominent example. In
permissionless systems, users can access the network and blocks anonymously
without registration. The network is open for anyone to join or exit freely.
The public chain is a decentralized blockchain, ensuring transaction security
and immutability through cryptographic (asymmetric encryption) algorithms and
establishing mutual trust in a network environment with consensus mechanisms.
Common consensus mechanisms in public chains include Proof of Work (POW) and
Proof of Stake (POS). In contrast, in permissioned blockchains, the prevalent
consensus protocols are Kafka, Raft, and PBFT.

Blockchain’s versatility is demonstrated through its applications in sectors
like healthcare, where it enhances security, privacy, and interoperability in
Electronic Health Record (EHR) systems shi2020applications . Additionally,
blockchain has been studied in the context of cloud computing, addressing
security and privacy challenges when outsourcing computational tasks to cloud
service providers shan2018practical . Additionally, Unmanned Aerial Vehicles
(UAVs) pokhrel2021blockchain , smart city esposito2021blockchain , cloud
computing gai2020blockchain ,edge computing xiong2018mobile , Internet of
Vehicles (IoV) kang2019toward , these examples demonstrate the broad range of
applications for blockchain technology and its ability to address diverse
challenges across different industries.

###  2.2 Privacy Preserving in Federated Learning

With the development of database technology and network technology, all kinds
of industries have accumulated a large amount of useful data. How to extract
valuable knowledge for decision-making from these data has become a top
priority. Positioned as a potent data analysis tool, data mining excels in
uncovering latent patterns and regularities within data, presenting findings
in the form of rules, clusters, decision trees, dependency networks, or other
knowledge representations. These insights find applications in diverse areas
such as business decision-making, scientific research, and medical
investigations.

FL initializes model parameters for all clients via a central server, as shown
in Figure 2. The client trains the local model with the initialized model
parameters and shares the parameters trained by the local model to the central
server. The central server aggregates the parameters of the local model and
sends the updated model and parameters to each client. Repeat the above steps
until the model converges.

Figure 2: Federated Learning architecture and its applications.

FL is divided into centralized FL and decentralized FL according to the
network topology. According to data availability, it can be divided into cross
silo FL and cross device FL li2021survey . In different data partition
scenarios, FL can be divided into horizontal FL, vertical FL and federated
transfer learning. Among them, the optimization algorithms of FL include
fedavg, smc-avg, fedprox etc.

  * •

Data Heterogeneity: Data heterogeneity is a significant challenge in FL due to
the decentralized nature of the approach. In FL, data is distributed across
multiple devices or entities, each with its unique characteristics, formats,
and representations. Firstly, feature heterogeneity arises when participating
devices have different sets of features or attributes available for training,
making the aggregation and alignment of models challenging. Secondly, data
distribution heterogeneity occurs due to variations in user populations,
geographical locations, or data collection practices among participating
devices, potentially introducing biases into the trained model. Lastly, data
format heterogeneity poses challenges in FL as data from different sources or
platforms may have varying formats, representations, or structures.

To address these heterogeneity challenges, researchers employ various
techniques ye2023heterogeneous , including model architecture adjustments and
feature engineering to harmonize feature spaces across devices. They utilize
meta-learning and domain adaptation to manage data distribution variations and
explore models with intermediate representations or multi-format designs for
data format heterogeneity. These strategies facilitate effective data use in
federated learning, enhancing model robustness and efficiency across diverse
environments.

  * •

Privacy and Security: FL is a decentralized approach to machine learning where
multiple devices or entities collaboratively train a shared model without
sharing their raw data. While FL offers numerous advantages, such as
preserving data privacy and reducing communication costs, it also raises
important privacy concerns yin2021comprehensive .

    * –

Data Leakage: During the training process, models are shared among
participating devices or entities. There is a risk of unintentional data
leakage if the models contain sensitive information about the training data.
Adversaries could potentially reconstruct or infer sensitive data from the
shared models.

    * –

Membership Inference Attacks: In a membership inference attack, adversaries
aim to determine whether a specific data point was part of the training
dataset. By analyzing the model’s responses to queries, adversaries can infer
the presence or absence of particular data points, potentially revealing
sensitive information about individuals.

    * –

Model Poisoning Attacks: In a model poisoning attack, adversaries inject
malicious data or manipulate their local updates to poison the shared model.
This can lead to the model incorporating biased or incorrect information,
compromising the privacy of other participants’ data.

To mitigate these threats, techniques such as Differential Privacy (DP), which
adds noise to the data or model updates to obscure individual contributions,
and Homomorphic Encryption (HE), allowing computations on encrypted data, are
applied. These methods ensure that FL remains resilient against attacks while
maintaining data privacy and model integrity. The focus of many research
endeavors has been on bolstering security and enhancing privacy protection
within the realm of FL. For instance, a scalable production system has been
outlined in bonawitz2019towards , which is tailored for the mobile device
domain, tackling challenges related to device availability, unreliable
connections, cross-device coordination, and limited resources. A different
framework is introduced in li2021ditto , which employs regularization terms to
amplify the fairness and robustness of personalized FL. In truex2019hybrid ,
differential privacy and homomorphic encryption have been incorporated into
FL, aiming to diminish privacy noise and enhance model accuracy via secure
multi-party computation. The TrustFL scheme zhang2020enabling  leverages
Trusted Execution Environments (TEE) to secure the trustworthy execution of
training tasks, offering high-confidence guarantees while preserving
efficiency. These various methodologies collectively contribute to enhancing
the security and privacy of FL. Secure model aggregation and performance
optimization also emerge as crucial areas of research. A distributed framework
enabling joint association and resource allocation is discussed in
khan2021dispersed , paving the way for multiple groups to learn a global
model. In addition, a model-contrastive FL framework is proposed li2021model ,
which improves local training performance by accounting for the similarity of
model representations. The framework proposed in pandey2020crowdsourcing
coordinates multiple mobile clients via an MEC server for parameter
aggregation and global model updates. Finally, a Bayesian nonparametric neural
network framework for FL yurochkin2019bayesian , where the global model is
constructed using the Probabilistic Federated Neural Matching (PFNM) method to
tackle communication issues. Each of these approaches contributes uniquely to
the secure model aggregation and performance optimization in FL.

  * •

Traceability and Accountability: Traceability and accountability are critical
considerations in FL to ensure transparency, integrity, and responsible use of
data.

    * –

Traceability involves model auditing, which tracks and audits the models
trained in FL by recording metadata such as model architecture,
hyperparameters, and data sources. Data provenance is another aspect of
traceability, enabling the tracking of the origin and history of the data used
for training, ensuring authenticity, and assessing potential biases. These
traceability measures enhance transparency and build trust among participants.

    * –

Accountability in FL encompasses participant accountability, where
participants are expected to adhere to agreed-upon protocols, privacy
measures, and security practices. Participants should be held accountable for
their actions to maintain the integrity of the FL process. Moreover,
maintaining security and trust in FL systems requires participants to
implement appropriate security measures, protect data confidentiality, and
prevent unauthorized access or malicious activities.

However, traceability and accountability in FL face several challenges. The
decentralized structure of FL introduces complexity in coordinating and
establishing consensus on traceability standards and accountability mechanisms
across multiple participants. Ensuring traceability and accountability while
preserving privacy adds another layer of complexity, as privacy-preserving
techniques must balance providing traceability information while protecting
sensitive participant data. Additionally, data fragmentation caused by the
distribution of data across different participants, poses challenges in
tracing the origin and lineage of data.

###  2.3 The Necessity of Integrating Blockchain and Federated Learning

Combining blockchain and FL has the potential to offer several advantages and
address key challenges in both domains:

  * •

Incentive Mechanisms: The cryptocurrency or token system native to blockchain
can facilitate the establishment of incentive mechanisms for participants in
FL. In traditional FL, due to economic rationality, many clients are reluctant
to share their valuable data. By rewarding data contributors, model
validators, and other participants with tokens, blockchain can incentivize
active participation, data sharing, and model improvement liu2020fedcoin .

  * •

Enhanced Data Privacy and Security: The decentralized and immutable nature of
blockchain offers a secure and transparent framework for data sharing and
storage. The traditional FL framework heavily relies on a single central
server and may fall apart if such a server behaves maliciously. At the same
time, the existing design is vulnerable to the malicious clients that might
upload poisonous models to attack the FL network. Incorporating the
cryptographic methods of blockchain into Federated Learning (FL) significantly
bolsters data privacy and security. Blockchain’s decentralized and immutable
nature ensures that data remains encrypted and anonymous, safeguarding against
the vulnerabilities of centralized servers and malicious model uploads by
clients. By leveraging blockchain, FL can achieve a transparent framework for
data sharing and storage, enabling participants to validate the integrity and
authenticity of shared models without exposing sensitive information
moudoud2021towards ; kang2020reliable ; li2021local . This integration
enhances trust and reliability within the FL ecosystem.

  * •

Trust and Transparency: The transparency and auditability inherent in
blockchain can tackle trust issues in FL. It enables participants to track the
history and provenance of data, models, and computations. This transparency
fosters trust among participants, as they can verify the fairness and
reliability of the FL process yang2023explainable .

The application of Blockchain in FL is exemplified by various frameworks and
systems designed to enhance privacy, accuracy, and trust:

  * •

BML-ES tian2021blockchain : A blockchain-centric machine learning framework
for Industrial Internet of Things (IIoT) edge services, utilizing smart
contracts for aggregation strategies and employing the SM2 public key
cryptosystem to secure privacy and improve model accuracy.

  * •

TrustFed ur2021trustfed : Integrates blockchain in cross-device FL systems to
prevent model poisoning, ensure fair training, and maintain the reputation of
participants. Smart contracts are used to manage reputations and exclude
malicious actors, ensuring a dependable training environment.

  * •

State Channels for Trust Supervision zhang2021federated : A mechanism that
uses blockchain and FL to create a trusted environment for distributed data
sharing. It employs state channels to establish secure sandboxes for FL tasks,
ensuring integrity and supervision throughout the process.

  * •

Proof of Federated Training (PoFT) chakraborty2022proof : A framework enabling
verifiable model training across blockchain networks, enhancing transparency
and trust in the collaborative training process.

  * •

Decentralized Model Training and Gradient Aggregation zhao2021blockchain :
Proposes a blockchain-based architecture for secure model training and
introduces a gradient aggregation method aimed at enhancing model accuracy,
privacy, and performance.

  * •

Blockchain-in-the-loop FL mothukuri2021fabricfl : Merges traditional FL with
Hyperledger Fabric, incorporating gamification to enhance participation and
efficiency.

Smart contracts within these systems play a pivotal role in ensuring fairness
and dependability by automating enforcement of agreements and conditions
without the need for intermediaries. This automation ensures that all parties
adhere to the predefined rules, significantly reducing the risk of biased or
malicious behavior. As a result, smart contracts contribute to creating a
transparent, secure, and trustworthy environment for FL, facilitating its
application in diverse fields such as smart cities zheng2022applications ,
vehicular communication networking pokhrel2020decentralized , edge FL
hu2021blockchainB , precision medicine warnat2021swarm ; wang2021blockchain ,
thereby enhancing privacy, security, and trust in FL ecosystems
Ouyang2023Artificial .

##  3 Blockchain-based Federated Learning: A Taxonomy and Review

Figure 3: Blockchain-based Federated Learning Architecture.

From the architecture of the FL in figure 2 we can realize that the aggregator
server suffers from the single point of failure. However, with the adaptation
of blockchain technology into FL, some security assumptions for FL need to be
declared in advance. First, aggregator may behave dishonestly or external
attacks could influence the FL result. Therefore, numerous works have been
done by combining blockchain and FL technology in order to prevent malicious
or honest-but-curious aggregator chen2018machine .

Usually, the architecture of this kind of BCFL is shown as figure 3. From a
bottom-up perspective, the overall architecture is divided into five layers.
The bottom layer of the architecture is infrastructure layer which contains
various kind of storage and computation resource. We will discuss and a
taxonomy will be provided in section 3.1. Section 3.2 will discuss the network
layer of blockchain based FL, which mainly includes P2P, random leader
election etc. There are mainly two ways in communication layer which contains
message and RPC protocol. In the algorithm layer, privacy preserving methods
and incentive mechanism are provided in this layer. Then the upmost layer is
related application. Besides, the blockchain consensus plays a vital role in
the whole architecture.

###  3.1 Architectures of Blockchain-based Federated Learning

BCFL Systems are categorized into three distinct architectures based on their
operational dynamics wang2021blockchain ; zhu2023blockchain : fully coupled,
where clients double as both training and blockchain nodes, offering
decentralization but requiring high device performance; flexibly coupled,
which separates blockchain and FL operations to ease network communication,
achieved through committee selection or smart contracts; and loosely coupled,
prioritizing reputation to gauge participant reliability, focusing mainly on
model update validation and reputation management on the ledger.

###  3.2 Infrastructure Layer

The BCFL architecture incorporates FL into blockchain in two primary
configurations: the L1 and L2 layer architectures. The L1 layer directly
integrates FL with blockchain, offering a decentralized, P2P framework where
peers join freely, supported by consensus mechanisms for system reliability
shayan2020biscotti . The L2 layer, alternatively, builds FL atop blockchain
nodes, emphasizing layered data processing and model training
bhattacharya2019bindaas .

At its core, the BCFL infrastructure unites data storage and computational
resources. It adopts a decentralized storage model, with privacy ensured
through advanced cryptographic methods like SMPC and homomorphic encryption.
These technologies facilitate secure, private data sharing and model
aggregation, while blockchain’s auditability improves transparency and trust.
Computational demands are met locally, utilizing Central Processing Unit
(CPUs), GPUs 2012cuda , and Tensor Processing Units (TPUs) 2017tpu . The
system addresses device heterogeneity che2022federated  and computational
limits through lightweight architectures, model compression jia2023efficient ,
and federated distillation, optimizing performance and resource allocation
across varied devices.

###  3.3 Network Layer

The network structure of blockchain inherits the general topology structure of
computer communication network, and can be divided into three categories:
centralized network, multi-centralized network and decentralized network as
shown in figure 4.

  * •

Centralized Networks: In a centralized network setup as shown in figure 4(a),
all communications and transactions are routed through a central node. This
structure, often seen in permissioned blockchain systems, can offer
streamlined efficiency and quicker consensus due to the singular control
point. However, it also presents a significant security risk; the central node
becomes a prime target for attacks, potentially compromising the entire
network’s integrity and privacy. For BCFL, this setup could limit the system’s
resilience and increase vulnerability to data breaches and single points of
failure.

  * •

Multi-Centralized Networks: Multi-centralized networks as shown in figure
4(b), or federated blockchains, introduce several central nodes instead of
just one. This setup is typically employed to balance control among multiple
organizations or parties, enhancing collaboration while still maintaining a
level of centralized governance. While this structure improves security and
reduces the risk associated with a single point of failure, it may still face
challenges in achieving the same level of decentralization and resistance to
censorship or collusion as fully decentralized networks. For BCFL systems,
multi-centralized networks can offer improved security and operational
efficiency but may still encounter scalability limits and centralized control
issues.

  * •

Decentralized Networks: Decentralized networks as shown in figure 4(c),
epitomized by permissionless blockchains like Bitcoin and Ethereum, distribute
data verification and transaction processing across a wide array of nodes.
This P2P network structure ensures no single point of control or failure,
significantly enhancing security and data integrity. Each node operates with
equal status, creating a robust system resistant to censorship, tampering, and
attacks. For BCFL, decentralized networks provide a secure and transparent
environment for data sharing and model training, although they may face
challenges in terms of scalability and consensus speed due to the distributed
nature of decision-making.

In summary, the choice of network setup in BCFL systems profoundly impacts
their performance and security. Centralized networks may offer operational
efficiency but pose higher security risks. Multi-centralized networks provide
a balance with improved security but still retain some centralized control
aspects. Decentralized networks, while offering the highest level of security
and data integrity, might struggle with scalability and slower consensus
mechanisms.

(a) centralized

(b) multi-centralized

(c) decentralized

Figure 4:  Blockchain network structure categories

###  3.4 Communication Layer

The communication layer plays a pivotal role in BCFL systems, orchestrating
data transfer and message exchanges among participants to facilitate the
collaborative learning process. It is tasked with transmitting model updates,
aggregated results, and coordination directives efficiently and securely
across the network of devices or servers engaged in FL.

The communication layer employs various protocols and mechanisms to ensure
efficient and secure data exchange.

  * •

Encryption Techniques: Advanced encryption methods are utilized to encrypt
data before transmission, safeguarding against unauthorized access and
ensuring that data privacy is maintained.

  * •

Network Protocols: Reliable and efficient network protocols are employed to
manage the delivery of messages. These protocols are designed to ensure that
data packets reach their intended destinations reliably and in order, even in
the face of network disruptions or congestion.

  * •

Synchronization Mechanisms: Given the distributed nature of BCFL systems,
synchronization mechanisms are crucial for coordinating communication among
participants liu2023distributed . These mechanisms ensure that model updates
are shared in a timely manner and help in managing the asynchronous nature of
data transmissions, aligning the updates from various participants.

  * •

Decentralized Communication Frameworks: Employing decentralized frameworks,
such as peer-to-peer (P2P) networks or blockchain, facilitates direct
communication between participants, eliminating the need for centralized
intermediaries. This approach not only enhances the system’s resilience and
decentralization but also reduces potential bottlenecks and points of failure.
For example, the Blockchain Assisted Decentralized FL (BLADE-FL) framework is
proposed in ma2020federated . This fully decentralized framework assigns
responsibility for both training and mining to full nodes. These frameworks
can enhance the decentralization and resilience of the FL system.

However, BCFL systems face significant challenges such as latency and limited
bandwidth, which are addressed through data compression techniques, to
minimize the size of data transmissions and enhance exchange speeds. Adaptive
network routing and congestion control algorithms optimize data flows,
reducing latency and improving communication efficiency. Additionally, batch
processing and caching are employed to lower the frequency and volume of data
transfers, mitigating the impact of network constraints. These strategies
ensure the communication layer in BCFL systems facilitates secure, efficient,
and resilient data exchange, overcoming the hurdles of distributed learning
environments and promoting seamless collaboration among network participants.

###  3.5 Algorithm Layer

In the algorithm layer of BCFL, there are four key aspects to consider:
Aggregation algorithms, security algorithms, optimization algorithms, and
incentive algorithms.

  * •

Aggregation Algorithms: These algorithms primarily address how to effectively
aggregate local models from different participants to form a global model. The
most common algorithm is FedAvg mcmahan2017communication , where participants
upload the weights of their local models to the blockchain network in each
round of training. The global model is then acquired by calculating the
average of these weights through smart contracts. Additionally, there are more
sophisticated aggregation algorithms, such as FedProx li2020federated ,
SCAFFOLD karimireddy2020scaffold , FedPD Zhang2020FedPD , and FedBN
li2021fedbn . In addition, by constructing an asynchronous FL system
liu2023aedfl ; liu2023fedasmu , it is possible to address the security
challenges posed by centralized models, achieving privacy, fault tolerance,
and reliable data sharing wang2022asynchronous . Based on this architecture, a
FL asynchronous aggregation protocol based on permissioned blockchain is
proposed that can effectively alleviate the synchronous FL algorithm
che2023federated ; liu2022multi ; jin2022accelerated ; zhou2022efficient  by
integrating the learned model into the blockchain and performing two-order
aggregation calculations.

  * •

Security Algorithms: These algorithms are dedicated to conducting model
training and aggregation while preserving the privacy of the participants. The
most common methods employ encryption technologies like homomorphic encryption
and secure multi-party computation to ensure the weights of the participants’
models are not leaked during the upload and aggregation process. Some methods
leverage characteristics of blockchain, such as decentralization and
immutability, to enhance the system’s resistance against attacks.

    * –

Secure Multi-Party Computation (SMPC): Secure Multi-Party Computation (SMPC)
is a computational model for protecting data privacy, allowing multiple
parties to compute without disclosing their private data. In SMPC, each party
holds a portion of private data, and computations can be performed on
encrypted data to maintain privacy.

In SMPC, parties communicate and interact via protocols, collectively
calculating the final result without having to directly expose their private
data. SMPC often involves the use of encryption techniques, cryptographic
protocols, and distributed algorithms to ensure data privacy and security.
During computation, parties can use technologies such as homomorphic
encryption, secret sharing, zero-knowledge proofs, and secure multi-party
computation to ensure the security of the computation process and results.

    * –

Homomorphic Encryption (HE): HE is an encryption algorithm that satisfies the
properties of homomorphic operations on ciphertexts. That is, after the data
undergoes homomorphic encryption, a specific calculation is performed on the
ciphertext, and the result of the ciphertext calculation, after corresponding
homomorphic decryption, is equivalent to the same calculation performed
directly on the plaintext data. This realizes a state of "computable but
invisible" for the data. By using homomorphic encryption technology,
computations can be carried out on ciphertexts without the need for a key,
which not only reduces communication costs but also balances the computational
costs among all parties.

    * –

Differential Privacy (DP): Differential privacy is a technology that protects
the underlying user privacy information in data by adding disruptive noise.
The principle ensures that even if an attacker has mastered all other
information except for one piece, they still cannot infer that piece of
information. The most common method is to add noise conforming to a certain
distribution to the result, randomizing the query result. The main issue to
address in differential privacy is data utility. Since it is necessary to
incorporate randomness into the query result, it could potentially lead to a
decrease in data usability li2021local .

    * –

  * •

Optimization Algorithms: These algorithms aim to optimize the performance of
FL, such as reducing the number of training rounds, communication overhead,
and enhancing model accuracy. The most common methods include gradient
compression and gradient pruning to reduce communication overhead. Other
techniques utilize asynchronous and local updates to decrease the number of
training rounds.

  * •

Incentive Algorithms: These algorithms consider how to motivate participants
to join in FL and distribute incentives fairly. Although FL has shown great
advantages in enabling collaborative learning while protecting data privacy,
it still faces an open challenge of incentivizing people to join the FL by
contributing their computation power and data. A Deep Reinforcement Learning
(DRL) based incentive mechanism as a solution addresses unique challenges of
unshared information and contribution evaluation difficulties in FL
zhan2020learning . Efficiency of this mechanism is demonstrated via numerical
experiments, compared with baseline approaches. On the other hand, cross-
disciplinary areas such as economics and game theory are also discussed in the
context of designing incentive mechanisms for FL tu2022incentive . The paper
elucidates various economic and game models, aiming to understand the
motivations behind their use in FL incentive mechanism design. It provides
detailed reviews, analyses, and comparisons of different economic and game
theoretic approaches for designing a variety of FL incentive mechanisms.

    * –

Reputation based FL: To address existing issues in federated learning, such as
redundant transmissions, network congestion, as well as security and privacy
concerns, reputation-based approaches have been proposed. Typically,
reputation-based methods involve measuring the reliability of participants by
designing a security mechanism. The blockchain-based reputation system
zhao2020mobile , which increments the reputation value of clients contributing
correct and useful model parameters and decrements for those uploading
malicious parameters, influencing client selection for subsequent training
rounds. A reputation mechanism framework RepBFL chen2021repbfl , merging
blockchain and FL for applications in the Internet of Vehicles (IoV). By
leveraging blockchain, it ensures shared data protection and the selection of
high-reputation nodes for FL, alongside evaluating the reliability of vehicles
in IoV. The approach presented in zhang2021blockchain  employs a reputation-
based evaluation using model quality parameters and blockchain to gauge worker
reliability and maintain reputation values. In lyu2020collaborative , research
on collaborative fairness in FL leads to the development of a collaborative
fair FL framework, CFFL. This introduces a reputation mechanism based on
empirical individual model performance, mediating participant rewards to
maintain fairness across communication rounds. Lastly, kang2019incentive
regards reputation as a metric quantifying the reliability and trustworthiness
of mobile devices. Its multi-weight subjective logic model is employed for
reputation calculation, with consortium blockchain technology securing
reputation storage in a decentralized manner. Notably, the reputation
calculation involves the task requester selecting eligible worker candidates
based on resource information. The reputation value of candidate workers is
then computed based on direct reputation feedback from interaction history and
indirect reputation feedback from other task requesters, all of which is
stored and managed on an open-access reputation blockchain.

    * –

Payment based FL: FedCoin liu2020fedcoin  employs Shapley Values (SVs) for a
feasible SV-based profit distribution that equitably mirrors contributions to
the global FL model. Here, the blockchain consensus entities deploy the
Shapley Proof-of-Stake protocol (PoSap) for the calculation of SVs and
creation of new blocks. Constructing an FL protocol on a public blockchain
network can resolve the challenges related to monitoring worker behavior and
guaranteeing protocol adherence toyoda2020blockchain . This protocol embeds
competition into BCFL, rewarding only those workers whose contributions are
valuable and naturally discouraging deviation from the protocol. The mobile-
crowd FL system jiang2022reward  incentivizes mobile devices to train accurate
models by offering rewards based on individual contributions. A Stackelberg
game models interactions between the server and devices, and two reward
policies, namely, the size-based and accuracy-based policies, are compared
under different definitions of individual contribution. In addition, the
challenge of transparently assessing contributions from different data owners
in a cross-silo horizontal federated learning setup is tackled by quantifying
data owners’ SV-based contributions with adjustable precision, safeguarding
their privacy ma2021transparent .

    * –

DeepChain: DeepChain weng2019deepchain  is a collaborative framework for
training deep learning models with joint participation from clients. It
guarantees data confidentiality, computational verifiability, and offers
incentives to participants. The incentive mechanism in DeepChain is
orchestrated around timeout checks and monetary penalties, fostering fairness
among participants. It takes punitive measures in scenarios where participants
fail to meet deadlines or inaccurately execute functions, by imposing monetary
penalties, confiscating the pre-deposited funds from dishonest participants
and redistributing them among honest ones.

    * –

Mechanism Design: Designing a mechanism is the objective for achieving
effective incentives. The survey zeng2021comprehensive  meticulously explores
incentive mechanisms for federated learning. It compiles existing incentive
mechanisms and categorizes them based on key techniques, such as the
Stackelberg game, auction, contract theory, Shapley value, reinforcement
learning, and blockchain. Furthermore, to address the challenges posed by
malicious participants in large-scale collaborations, Refiner is proposed
zhang2021refiner . The system resides on the Ethereum public blockchain
platform and operates an incentive mechanism rewarding participants based on
the volume of their training data and the performance of local updates. For
dealing with malicious actors, Refiner deploys a committee of randomly
selected validators. These validators penalize unscrupulous participants by
denying rewards and eliminating corrupt updates from the global model.

In BCFL, there is a need to craft an effective incentive mechanism that
stimulates active participation among participants and recognizes their
contributions. In addition, privacy-preserving methods should be employed to
protect data privacy of participants throughout the FL process. Subsequently,
its training process should be optimized for the system to achieve the best
performance. By addressing the above issues, BCFL systems can motivate
participants while ensuring data privacy and security.

###  3.6 Blockchain Consensus

In the context of FL system implementation, consensus pertains to the
agreement or protocol employed by participants to synchronize their models and
collectively make decisions. Consensus algorithms, such as PoW or PoS, are
commonly utilized in blockchain-based systems to establish agreement among
distributed participants. These algorithms ensure that all participants agree
on the validity of model updates and prevent malicious actors from tampering
with the system.

The consensus mechanism is to complete the verification and confirmation of
transactions in a very short period of time through the voting of special
nodes. If nodes with disparate interests can reach a consensus on a
transaction, it implies a broader network consensus. With the evolution of
blockchain technology, the term consensus mechanism has become widely
recognized, and various innovative consensus mechanisms continue to emerge.

Consensus holds paramount importance in blockchain technology as it safeguards
the integrity, security, and immutability of the distributed ledger. Numerous
consensus algorithms are employed in blockchain networks, each characterized
by its unique attributes and trade-offs.

  * •

Proof of Work (PoW): Bitcoin uses the PoW(Proof of Work) workload proof
mechanism, and later Ethereum is the PoW and PoS(Proof of Stake) consensus
mechanism. PoW is the equivalent of figuring out a difficult math problem that
gets harder over time. Although PoW is a consensus mechanism recognized by
everyone, computing consumes a lot of energy and may indirectly affect carbon
emissions and the environment. BCFL qu2020decentralized , a distributed hash
table for efficient block generation is introduced. This solution employs a
proof-of-work consensus mechanism to ensure consistency in the global model.
LearningChain chen2018machine , a decentralized federated system leveraging a
Byzantine fault-tolerant aggregation algorithm known as l-nearest aggression.
The system is based on the PoW consensus, where the leader is selected through
competition and the l-nearest algorithm is used to aggregate the gradient.
Swarm Learning (SL) warnat2021swarm  a decentralized machine learning approach
combining edge computing and blockchain-based P2P networking. With Swarm
Learning, data and parameters are kept at the edge, thereby eliminating the
need for a central coordinator.

  * •

Proof of Stake (PoS): PoS is seen as a more environmentally friendly
alternative to PoW. Instead of miners competing to solve problems, validators
are chosen to create new blocks based on the amount of cryptocurrency they
hold and are willing to "stake" as collateral.

  * •

Delegated Proof of Stake (DPoS): In DPoS, stakeholders elect a certain number
of delegates who validate transactions and create blocks. This method is
designed to be more democratic and efficient than traditional PoS.

  * •

Proof of Training Quality (PoQ): The existing consensus mechanisms, such as
Proof of Work (PoW), consume significant computational and communication
resources or have limited additional contributions to data sharing. To address
this problem, lu2019blockchain  proposes a consensus mechanism called PoQ has
been proposesd that combines FL with differential privacy. PoQ integrates data
model training with the consensus process, replacing the meaningless
computational work of finding random numbers in PoW with the authentication of
model parameter accuracy.

  * •

Byzantine Fault Tolerance (BFT): This consensus mechanism aims to withstand
’Byzantine’ faults, where components may fail and there is imperfect
information on whether a component is failed or not. There are several BFT,
such as Practical Byzantine Fault Tolerance (PBFT) used in Hyperledger Fabric
2018hyperledger ; sun2021permissioned ; chen2020methodology , and the
Federated Byzantine Agreement (FBA) used in Stellar mittal2021Stellar .

  * •

Proof of Federation (PoF): Biscotti shayan2020biscotti , which combines PoF
with consistent hashing and Verifiable Random Functions (VRF) to select
critical roles for peer nodes. These roles aid in coordinating the privacy and
security of model updates. To prevent peers from poisoning the model through
Multi-Krum defense, Biscotti employs differentiated private noise to provide
privacy. It also utilizes Shamir secret sharing for secure aggregation.
However, when all nodes participate in the consensus, the computational load
is too large ramanan2020baffle .

  * •

RAFT: RAFT is a consensus algorithm used in some permissioned blockchain
networks. It elects a leader among a group of nodes, and the leader is
responsible for proposing and validating blocks kim2021improved . Raft focuses
on simplicity and fault tolerance and is designed to be easier to understand
and implement than other consensus algorithms.

  * •

Proof of Federated Training: Proof of Federated Training (PoFT)
chakraborty2022proof , is a framework for enabling verifiable model training
across multiple blockchain networks. It addresses issues such as power
consumption/resource wastage in POW and data privacy in blockchain qu2021proof
. In addition, Proof of FL (PoFL) is also employed in vehicular networks,
where vehicles compete to become miners by adhering to the FL consensus proof
within the blockchain network ayaz2021blockchain . Additionally, IPFS and PoFL
are utilized to ensure decentralized federated learning security for connected
autonomous vehicles he2021bift .

  * •

Committee Consensus: The Blockchain-based FL framework with Committee
consensus (BFLC) li2020blockchain , utilizes blockchain for global model
storage and local model update exchange, eliminating the need for a
centralized server liu2023heterps . Additionally, an innovative committee
consensus mechanism is introduced to reduce the computational load and
mitigate malicious attacks.

These consensus algorithms all have their strengths and weaknesses and are
suited to different use-cases. Selecting the right consensus mechanism is
vital for the security, scalability, and efficiency of the blockchain network.
These are some commonly used blockchain consensus algorithms. However, the
selection of a consensus algorithm is influenced by factors such as the
desired degree of decentralization, security, scalability, and the specific
needs of the blockchain network.

###  3.7 Application Layer

FL has various applications across different domains where services based on
this technology can be provided.

  * •

Internet of Vehicles: Vehicles are increasingly becoming data generation
sources. Data such as GPS location, speed, and road conditions can contribute
to better traffic management, route planning, and accident prevention.
However, this data is also sensitive bai2022internet . BCFL enables
aggregation of data from multiple vehicles to train models without sharing the
raw data pokhrel2020decentralized ; chai2020hierarchical ; chen2021bdfl ;
otoum2020blockchain ; chen2021repbfl ; li2022fedhisyn . Moreover, blockchain
can also be used to maintain a tamper-proof record of vehicle interactions and
transactions in the network aloqaily2021energy ; lu2020blockchain .

  * •

Resource allocation: In large distributed systems, effective resource
allocation is critical to maximize efficiency wang2022incentive . By applying
FL on top of a blockchain network, resources can be allocated dynamically
based on the learning from the network usage patterns li2021blockchain . In
essence, integrating blockchain with FL not only fortifies privacy and
reliability but also provides a platform for efficient resource allocation and
utilization deng2021flex .

  * •

Edge computing: BCFL has great potential for applications in edge computing.
It provides a secure mechanism for data sharing, resource collaboration and
sharing, model updates and upgrades, as well as guarantees for interference
resistance and fault tolerance liu2021blockchain ; lu2020blockchain ;
nguyen2022latency ; nguyen2021federated ; qu2020decentralized . By leveraging
the computing resources and data of edge devices, BCFL enables intelligent
applications to perform inference and decision-making tasks efficiently and
securely rahmadika2021blockchain ; lu2020low ; lu2020communication . It
addresses challenges such as data privacy, resource constraints, and unstable
environments commonly encountered in edge computing wan2022privacy ;
hu2021blockchain . This approach offers a novel solution for the development
and expansion of edge computing scenarios. It has been extensively researched
and applied in the Mobile phones scenario, and it is believed that it will
provide solutions for the development and expansion of more edge computing
scenarios in the future fan2022mobile ; feng2021two ; hu2021blockchain ;
hu2021blockchainB ; kong2021achieving .

  * •

Healthcare: In healthcare, patient data is sensitive but can be extremely
useful for detecting diseases kumar2021blockchain ; passerat2019blockchain ;
mohammed2023energy  and improving treatment rahman2020secure ;
myrzashova2023blockchain . BCFL allows healthcare institutions to collaborate
and learn from a vast amount of patient data without compromising patient
privacy passerat2020blockchain ; aich2022protecting ; zhang2021blockchain2 .
In addition, blockchain can provide traceability of data and computations,
increasing the trust in the learned models wang2021blockchainMedical .

  * •

Energy: The integration of blockchain with FL presents a transformative
approach for diverse energy applications. BCFL can be used to optimize grid
operations, facilitates P2P energy trading and sharing across microgrids
bouachir2022federatedgrids . Moreover, in the context of the Industrial
Internet of Things (IIoT), BCFL can address security and privacy concerns
associated with credit data sharing in wireless networks yang2022privacy ;
otoum2022federated ; issa2023blockchain ; Huang2023Distance ;
singh2023fusionfedblock . It is also being applied for blade icing detection
in wind energy turbines, a rapidly growing sector of renewable energy
cheng2022blockchain .

##  4 Challenges and Open Research Directions

The amalgamation of blockchain with FL combines the advantages of both realms,
establishing a system that prioritizes data privacy and security.
Nevertheless, this integration presents its own array of challenges. In this
section, we outline these challenges and propose potential research
directions.

###  4.1 Data Security and Privacy Protection

In a landscape where FL with blockchain, the paramount concern becomes
ensuring the security and privacy of data. Effectively managing sensitive
information, such as medical records or personal identities, within a
networked environment is a challenge. An in-depth exploration of cryptographic
techniques to handle such data without exposing its true content is essential.
Future research should delve into new cryptographic solutions tailored to this
integrated system.

###  4.2 Model Efficiency and Performance Optimization

Efficient computation for distributed data and models is a fundamental
requirement of FL. The core challenges in this domain involve devising
algorithms that reduce computational complexity, minimize communication
overhead, and enhance the efficiency of model training and inference. Future
research endeavors should concentrate on fine-tuning distributed optimization
methods, integrating advanced compression strategies zhang2022fedduap  to
alleviate communication burdens, and exploring hardware enhancements for
enhanced computational efficiency. An investigation into hybrid models,
amalgamating centralized and decentralized training methods, also holds
considerable promise.

###  4.3 Scalability

As blockchain and FL converge towards creating secure, private data management
frameworks, scalability emerges as a crucial bottleneck, particularly as the
systems scale and demand increases. The integration of off-chain calculations,
sidechains, and layer-2 technologies like state channels or Plasma represents
a forward-thinking approach to overcoming these challenges. Off-chain
calculations offload intensive tasks, reducing main blockchain load, while
sidechains manage transactions separately to decrease congestion. Layer-2
technologies, such as state channels or Plasma, facilitate fast transactions
atop the existing blockchain, maintaining security. These approaches,
leveraging blockchain’s and FL’s core principles, aim to cultivate scalable,
secure, and decentralized learning ecosystems.

##  5 Conclusion

As a decentralized and immutable technology, blockchain enables an egalitarian
platform for all network participants, mitigates data breaches, and fosters
trustworthiness. The combination of FL with blockchain creates a robust
learning ecosystem prioritizing data security and user privacy. In this paper,
we conduct a thorough exploration of such integrations, providing a
comprehensive perspective on FL architectures informed by blockchain. We aim
to offer clear insights for researchers and practitioners in this burgeoning
field by examining the foundational elements of both realms and highlighting
their synergies. We are optimistic that the challenges and research pathways
outlined herein will guide the next wave of innovations in decentralized
machine learning frameworks.

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