Table 10: Statistics of the jerk values. The results for the mean and median are omitted as these values converge to zero. |  |  Longitudinal Jerk $k_{x}$ |  |  Lateral Jerk $k_{y}$
---|---|---|---|---
Statistic | Avg | Mean | SD | Min | Max |  | Mean | SD | Min | Max
Absmax | mean | 19.611 | 1.997 | 14.356 | 24.020 |  | 33.401 | 3.525 | 25.763 | 46.110
| median | 17.355 | 1.657 | 12.977 | 20.726 |  | 29.459 | 3.300 | 24.095 | 38.331
Max | mean | 17.024 | 1.721 | 12.698 | 20.795 |  | 29.589 | 3.230 | 22.543 | 39.063
| median | 15.483 | 1.452 | 11.599 | 19.833 |  | 26.604 | 2.905 | 20.587 | 33.588
Min | mean | -17.215 | 1.859 | -21.722 | -12.794 |  | -29.236 | 3.108 | -38.831 | -23.955
| median | -15.390 | 1.481 | -18.723 | -12.550 |  | -26.255 | 2.977 | -32.913 | -21.127
SD | mean | 4.756 | 0.372 | 3.831 | 5.432 |  | 8.195 | 0.720 | 6.866 | 9.956
| median | 4.652 | 0.352 | 3.822 | 5.577 |  | 7.887 | 0.688 | 6.762 | 9.536
RMS | mean | 4.760 | 0.373 | 3.834 | 5.437 |  | 8.198 | 0.720 | 6.869 | 9.962
| median | 4.654 | 0.352 | 3.822 | 5.580 |  | 7.889 | 0.689 | 6.762 | 9.544
IDR | mean | 5.561 | 0.391 | 4.695 | 6.312 |  | 9.387 | 0.787 | 7.565 | 11.129
| median | 5.441 | 0.376 | 4.570 | 6.422 |  | 9.014 | 0.797 | 7.251 | 11.416
Table 11: Statistics of the distance to lane center and relative drift indicators. |  |  Distance to Lane Center $d_{\mathrm{CL}}$ |  |  Relative Drift Velocity $v_{\mathrm{drift}}$
---|---|---|---|---
Statistic | Avg | Mean | SD | Min | Max |  | Mean | SD | Min | Max
Absmax | mean | 0.398 | 0.058 | 0.277 | 0.552 |  | 1.605 | 0.175 | 1.198 | 1.975
| median | 0.384 | 0.055 | 0.262 | 0.509 |  | 1.452 | 0.163 | 1.147 | 1.880
Max | mean | 0.095 | 0.048 | 0.020 | 0.241 |  | 1.212 | 0.137 | 0.887 | 1.506
| median | 0.086 | 0.054 | -0.003 | 0.222 |  | 1.070 | 0.137 | 0.747 | 1.349
Min | mean | -0.350 | 0.063 | -0.513 | -0.192 |  | -1.436 | 0.172 | -1.852 | -1.100
| median | -0.350 | 0.061 | -0.476 | -0.200 |  | -1.300 | 0.157 | -1.654 | -1.006
Mean | mean | -0.149 | 0.053 | -0.272 | 0.014 |  | -0.104 | 0.062 | -0.261 | 0.032
| median | -0.149 | 0.055 | -0.268 | 0.023 |  | -0.073 | 0.053 | -0.196 | 0.035
SD | mean | 0.128 | 0.018 | 0.086 | 0.163 |  | 0.752 | 0.084 | 0.568 | 0.952
| median | 0.123 | 0.018 | 0.092 | 0.161 |  | 0.657 | 0.070 | 0.522 | 0.851
RMS | mean | 0.243 | 0.042 | 0.154 | 0.348 |  | 0.790 | 0.090 | 0.610 | 1.002
| median | 0.226 | 0.040 | 0.142 | 0.314 |  | 0.689 | 0.074 | 0.529 | 0.881
IDR | mean | 0.195 | 0.029 | 0.132 | 0.248 |  | 1.142 | 0.146 | 0.854 | 1.467
| median | 0.181 | 0.030 | 0.113 | 0.236 |  | 0.927 | 0.109 | 0.775 | 1.192
Median | mean | -0.154 | 0.056 | -0.290 | 0.027 |  | -0.096 | 0.070 | -0.271 | 0.019
| median | -0.153 | 0.060 | -0.279 | 0.042 |  | -0.058 | 0.053 | -0.209 | 0.035
Table 12: Class percentages and curve cutting intensities of the transient cornering behavior. | Class Percentage |  |  Intensity $\acute{I_{T}}$
---|---|---|---
Trajectory Class | Mean | SD | Max |  | Mean | SD | Max
Center | 22.36 | 10.73 | 55.00 |  | 0.87 | 2.57 | 12.66
Early Cutting | 4.17 | 4.58 | 20.00 |  | 17.33 | 27.68 | 166.45
Early Counter | 4.81 | 4.71 | 17.39 |  | 26.83 | 80.42 | 612.72
Late Cutting | 6.90 | 6.27 | 24.00 |  | 29.07 | 37.03 | 178.55
Late Counter | 1.17 | 2.19 | 8.33 |  | 6.04 | 15.07 | 83.12
Cutting | 16.47 | 9.87 | 37.50 |  | 82.98 | 197.17 | 1576.52
Counter | 2.78 | 3.83 | 12.50 |  | 18.23 | 40.29 | 252.54
Severe Cutting | 1.86 | 3.67 | 16.67 |  | 22.28 | 57.70 | 281.86
Severe Counter | 0.28 | 1.55 | 10.00 |  | 1.15 | 6.55 | 44.86
Biased Inner | 31.96 | 13.99 | 66.67 |  | 217.03 | 114.58 | 625.94
Biased Outer | 1.46 | 2.90 | 13.04 |  | 55.59 | 122.60 | 450.57
Oscillating | 0.49 | 1.38 | 5.26 |  | 2.85 | 9.00 | 46.14
Oscillating Cutting | 2.79 | 4.22 | 20.00 |  | 28.57 | 54.08 | 269.82
Oscillating Counter | 1.03 | 2.41 | 11.11 |  | 16.08 | 55.46 | 297.45
Slow Severe Cutting | 0.31 | 1.24 | 6.67 |  | 1.19 | 5.16 | 28.80
Slow Severe Counter | 0.08 | 0.61 | 4.76 |  | 0.45 | 3.57 | 28.09
For percentage, all min values are zero except for the center class ($=5\%$).
Intensity values are multiplied by $1000$ to account for low values.
Table 13: Loadings of the mean PCA of the proposed G-G envelope. | Loadings |
---|---|---
Angle | PC1 | PC2 | Uniqueness
-165 | 0.28 | 0.64 | 0.51
-150 | 0.31 | 0.69 | 0.43
-135 | 0.32 | 0.76 | 0.32
-120 | 0.26 | 0.76 | 0.35
-105 | 0.38 | 0.57 | 0.54
-90 | 0.46 | 0.36 | 0.65
-75 | 0.50 | 0.38 | 0.60
-60 | 0.27 | 0.67 | 0.48
-45 | 0.05 | 0.66 | 0.57
-30 | -0.05 | 0.76 | 0.42
-15 | 0.42 | 0.49 | 0.59
0 | 0.48 | 0.61 | 0.40
15 | 0.46 | 0.52 | 0.52
30 | 0.73 | 0.37 | 0.32
45 | 0.87 | 0.21 | 0.20
60 | 0.91 | 0.21 | 0.13
75 | 0.89 | 0.24 | 0.15
90 | 0.92 | 0.16 | 0.13
105 | 0.91 | 0.17 | 0.14
120 | 0.86 | 0.23 | 0.21
135 | 0.82 | 0.22 | 0.28
150 | 0.77 | 0.42 | 0.24
165 | 0.60 | 0.47 | 0.42
180 | 0.62 | 0.40 | 0.46
Note: Varimax rotation was used

  *[AVs]: Autonomous Vehicles
  *[LDW]: Lane Departure Warning
  *[LKS]: Lane Keeping Systems
  *[DBQ]: Driver Behavior Questionnaire
  *[DBI]: Driving Behaviour Inventory
  *[DSQ]: Driving Style Questionnaire
  *[DVQ]: Driving Vengeance Questionnaire
  *[MDSI]: Multidimensional Driving Style Inventory
  *[PSS]: Perceived Stress Scale
  *[DEQ]: Driver Evaluation Questionnaire
  *[DSEQ]: Driver Self-Evaluation Questionnaire
  *[RDHS]: Reckless Driving Habits Scale
  *[CAS]: Coronavirus Anxiety Scale
  *[AQ]: Aggression Questionnaire
  *[DAS]: Driving Anger Scale
  *[PCA]: principal component analysis
  *[CCG]: curve cutting gradient
  *[ANOVA]: analysis of variance
  *[SD]: standard deviation
  *[RMS]: root mean square
  *[IDR]: interdecile range


# Trust-as-a-Service: A reputation-enabled
trust framework for 5G networks

José María Jorquera Valero, Pedro Miguel Sánchez Sánchez, Manuel Gil Pérez,
Alberto Huertas Celdrán, and Gregorio Martínez Pérez José María Jorquera
Valero, Pedro Miguel Sánchez Sánchez, Manuel Gil Pérez, and Gregorio Martínez
Pérez are with the Department of Information and Communications Engineering,
University of Murcia, 30100 Murcia, Spain (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>(Corresponding
author: José María Jorquera Valero)Alberto Huertas Celdrán is with the
Communication Systems Group (CSG) at the Department of Informatics (IfI),
University of Zurich UZH, 8050 Zürich, Switzerland (e-mail:
huertas@ifi.uzh.ch).

###### Abstract

Trust, security, and privacy are three of the major pillars to assemble the
fifth generation network and beyond. Despite such pillars are principally
interconnected, they arise a multitude of challenges to be addressed
separately. 5G ought to offer flexible and pervasive computing capabilities
across multiple domains according to user demands and assuring trustworthy
network providers. Distributed marketplaces expect to boost the trading of
heterogeneous resources so as to enable the establishment of pervasive service
chains between cross-domains. Nevertheless, the need for reliable parties as
“marketplace operators” plays a pivotal role to achieving a trustworthy
ecosystem. One of the principal blockages in managing foreseeable networks is
the need of adapting previous trust models to accomplish the new network and
business requirements. In this regard, this article is centered on trust
management of 5G multi-party networks. The design of a reputation-based trust
framework is proposed as a Trust-as-a-Service (TaaS) solution for any
distributed multi-stakeholder environment where zero trust and zero-touch
principles should be met. Besides, a literature review is also conducted to
recognize the network and business requirements currently envisaged. Finally,
the validation of the proposed trust framework is performed in a real research
environment, the 5GBarcelona testbed, leveraging 12% of a 2.1GHz CPU with 20
cores and 2% of the 30GiB memory. In this regard, these outcomes reveal the
feasibility of the TaaS solution in the context of determining reliable
network operators.

###### Index Terms:

Trust models, zero trust, trustworthiness relationships, distributed
marketplace, 5G

##  I Introduction

Among all pillars of the communication evolution, fifth generation
technologies play a paramount role as cutting-edge network paradigms, from
radio technology and optical networks to non-terrestrial network
communications and ubiquitous computing. Such paradigms in turn bring
challenges to be overcome by next-generation technologies such as reduction of
energy footprint, multi-tenancy, extreme-reliable communication, automated
management and orchestration, on-demand service and resource allocation, and
trustworthy infrastructures, among others [1].

In 5G and beyond, the distributed marketplaces encompass a potential solution
commonly utilized by the vertical industry to cater for end-to-end composite
services or slices that allow satisfying all requirements and Key Performance
Indicators (KPIs) in terms of coverage, networking and computing resources,
and Virtual Network Functions (VNFs). Since such heterogeneous services and
resources may be supplied by a single provider or multi-party collaboration
across several domains, it is paramount to elect a trustworthy network
provider, which ensure the fulfillment of requirements and KPIs, and guarantee
a trustworthy environment. In this regard, trust models facilitate reliable
establishments among different stakeholders predicting a forthcoming trust
score.

Nevertheless, trust models need to progress over time as novel network and
business requirements are constantly appearing and they cannot be covered by
prior trust models [2]. 5G and beyond are envisioned as compounded networks in
which an end-to-end communication will entail multiple entities from the same
or different locations and domains. Thus, trust models ought to contemplate
reliable end-to-end chains to predict future behaviors of all implicated
entities from the origin to the end. In the same manner, implicit trust should
not be granted to stakeholders, regardless of whether they are placed in an
intra- or inter-domain scenario, as trust by default is a potential attack
vector exploited by spiteful entities. In this sense, a zero trust approach,
driven by the NIST [3], is a predominant principle for imminent trust models
to dwindle the attack surface. Another fundamental requirement is the
minimization of human interaction in the trust model lifecycle management,
also known as zero-touch approaches. Trust models should spur the
automatization of network and service management via high-level policies,
triggers, and artificial intelligence algorithms. Simultaneously, the
automation process also entails an essential effort to enable easier
integration with other B5G network orchestration and management components;
for instance, a distributed marketplace allowing verticals to expose telco
digital assets and hire them to automatically satisfy user demands.
Nevertheless, these requirements are currently not all addressed at the same
time by the solutions in the literature [2].

Hence, the paper at hand analyzes the present literature to determine whether
the identified network and business requirements related to trust models are
being contemplated. Besides, it also presents a reputation-based trust
framework capable of guaranteeing a trustworthy ecosystem where stakeholders
can establish reliable end-to-end connections across domains as well as
dealing with the above-mentioned novel network requirements. In particular,
such a framework considers a set of product offers (POs), available in the
5GZORRO European project distributed marketplace [4], to be thoroughly
analyzed. Thereupon, an adapted PeerTrust model for peer-to-peer communities
is leveraged to predict both a provider and product offer trust scores from
historical interactions and recommendations, the latter published in a Data
Lake platform to be consulted by interested stakeholders.

The remainder of this article can be outlined as follows. Section II carries
out in-depth research into the utmost importance trust models applied to on-
demand service and resource provision environments. Section III describes the
design of our reputation-based trust framework. Then, Section IV presents the
performance assessment results of the trust framework. Finally, Section V
recaps some conclusions as well as ongoing works for future work.

##  II Related Work

In this section, we analyze the most newfangled approaches that explore trust
models as a mechanism to cater for reliable on-demand service or resource
capabilities in 5G and B5G. To determine the compatibility level of analyzed
approaches, we have compiled a set of universal network and business
requirements which should be shared between 5G trust model solutions such as
trustworthy end-to-end chains across domains and the zero trust and zero-touch
principles.

Trust remains a vital requirement in the cloud environment since reputable
relationships between consumers and providers may guarantee the fulfillment of
offered user’s Quality of Service (QoS) as well as dwindling the chance to
infringing Service Level Agreements (SLAs) or Smart Contracts (SCs) signed. In
addition, the current cloud environments share paramount characteristics with
the on-demand service and resource capability provisioning, in which our
reputation-based trust framework is entailed. For these reasons, we have
identified a set of researches that not only contemplate trust models as a
potential solution for cloud environments but also fit to some extent with the
requirements described above.

Hassan et al. proposed in [5] a QoS-based model to assess the trust of cloud
providers dynamically before each new interaction. The authors composed the
cloud resources’ trust as a blend of the provider reputation, from users’
feedback, plus the computing power at run-time, from SLA attributes. To
regulate dishonest feedback, the covariance technique was leveraged to
calculate user credibility and discover misleading feedback. Nevertheless,
their model did not consider requirements such as end-to-end chains (only
analyzing the last entity of the chain) or the zero trust principle (main
ideas are not supported through paper). To test their enhanced QoS-based
model, the authors contrasted their transaction success rate (TSR) against the
Armor model, obtaining 0.92 and 0.74 TSRs, respectively, when there was a 40%
of fake users’ feedback.

Concerning the business needs to scale their computing and infrastructure
capabilities up, a new term called Federated Cloud appeared to enable the
integration of public, community, and private clouds to support business
requirements. Thereby, an inappropriate selection of deployment cloud
platforms may encompass performance, security, and even legal issues. To cope
with them, Papadakis et al. presented in [6] a hybrid reputation-based trust
management (RTM) approach to evade selecting untrustworthy cloud applications
in federated scenarios. The authors leveraged KPIs and Quality of Experience
(QoE) metrics to continuously evaluate cloud providers, according to customer
deployment objectives and credibility. In this case, the biased assessments
were detected by the deviation of objective SLA monitoring values against a
customer’s rating. Additionally, the authors fulfilled pivotal requirements
such as automatization, by considering trust as a substep of the full
automated SLA lifecycle, and trustworthy end-to-end connections, by analyzing
reputation of both cloud infrastructure and applications. The experiments
showcased a 20% performance betterment thanks to the credibility mechanism. In
the same line, Latif et al. presented in [7] a federated cloud trust
management framework to insure the fulfillment of privacy laws and the
protection of customer’s data. As a result, they addressed the issues for
trust establishment and evaluation. Particularly, the framework was formed of
three dimensions: SLA parameters focused on security and privacy, feedback
from customers, and feedback from other clouds. Since the final reputation is
composed from multiple entities involved in the relationship, it can be
ensured end-to-end trustworthy chains. To test their framework, the authors
contrasted their trust scores against other existing schemes and their SLAs
such as IBM, Amazon, or Google, among others, reaching the second-best result
in the vast majority of cloud providers. Nonetheless, they did not describe
ideas aligned the zero trust principle so it cannot be guaranteed.

Also dealing with the trust in cloud environments, in [8], Khilar et al.
centered on ascertaining both customers’ trust prior to accessing the cloud
and resources’ trust. This approach was formed by multiple sub-modules among
which the available resource catalog can be highlighted as the starting point.
Like in [6] and in [7], customers and resources’ behavior, feedback, and SLA
parameters were contemplated to compute a trust score. As part of SLA
parameters, the customer’s satisfaction was formulated as the total number of
successful tasks. Furthermore, the authors ensured zero trust, full automation
of all steps, and an assessment of multiple entities involved in the end-to-
end chain, not only the final target. In terms of performance, the k-Nearest
Neighbors (kNN) got a 0.94 of precision, recall, and F1-Score as well as a
6.48% of mean absolute error (MAE), and 25.45% of Root MAE. Not only the
selection of trustworthy Cloud Providers (CPs) is critical but also the
possible co-tenants hosted in the cloud. In [9], Takur and Breslin proposed a
reputation-based management mechanism utilized by CPs to distinguish users’
behavior and properly assign resources based on trust scores. The CP
reputation was formulated as the capability and willingness to differentiate
between good and malicious users. Hence, a constant increase or decrease of
users’ reputation of a multi-tenancy group enhanced the CP reputation;
otherwise, the CP was not able to create homogeneous groups and its reputation
dwindled. In addition, the authors considered feasible rational, irrational,
and opportunistic reports by the CP, achieving a higher reputation when the
rational approach was met.

Another indispensable requirement to be fulfilled by 5G and beyond trust
models is the establishment of a reliable end-to-end chain. In this vein, Wang
et al. proposed in [10] a trust evaluation model for mobile edge nodes (TEM-
MENs) to guarantee a reliable node chain between the trustor and the trustee,
withstanding malicious attacks. Depending on the number of nodes, the authors
declared an atomic trust chain (without intermediate nodes) or a combined
trust chain. In the case of the atomic chain, they considered interaction
trust, energy trust, and recommendation trust, along with time windows. With
regard to the combined chain, they collected the previous values for each node
forming the path(s), which covers the end-to-end trustworthy chain
requirement. To automatically collect all information and fulfill the zero-
touch philosophy, an enhanced Dijkstra algorithm was employed. By mean of
several experiments, the TEM-MENs demonstrated to have the highest detection
rate (96%) and its runtime had a slope of 0.1, so it allowed better
adaptability. Similarly, Fan et al. also dealt with Service Function Chains
(SFC) in [11]. In particular, they designed a credibility-based deployment
strategy (CBDS) to prognosticate the trust of VNF nodes through the SFC
credibility. The credibility was formed by reliability, availability, and
authenticity properties. Additionally, the authors also added extra
functionalities such as adopted sliding windows and trigger mechanisms, which
empowered the mechanism as dynamic and fully automatic. In terms of
experiments, the CBDS reached a 90% acceptance rate for 0.75 credibility value
(the highest one).

Lastly, Debe et al. addressed in [12] the problem of ensuring trust in the
provision of compute and networking capabilities. Hence, the authors presented
a reputation-based solution to discover a trust score in a decentralized way.
A blockchain together with SCs allowed calculating the reputation of public
fog nodes from past interactions. Besides, the credibility of client IoT
devices, which was computed as a clustering of the most legitimate group of
vectors contrasting the rating rate with the majority, was also contemplated
to build the final trust of each public fog node. Similarly, reward and
punishment mechanisms were defined to regulate the weight of client feedback
and detect potential malicious clients.

As observed, only [8] meets the three principal requirements: the zero trust,
zero-touch, and trustworthy end-to-end chains at the same time, which are
paramount pillars to provide a reputation-based model compatible with future
networks. Yet, this approach did not cover the same objective as ours because
it was oriented to an access control cloud scenario and our objective is to
enable a trustworthy resource and service provisioning discovery for
distributed marketplaces. The proposed framework therefore aims to fill the
gap in reputation-based trust models for 5G networks, as well as to ensure an
automated, practical, and scalable framework.

##  III Reputation-based Trust Framework Design

This section describes the principal modules and characteristics of our Trust-
as-a-Service solution as well as the most important pillars to compute trust
scores. As shown in Figure 1, the trust framework is principally composed of
four sub-modules: the Information gathering and sharing, the Trust
computation, the Trust storage, and the Continuous update. Next subsections
thoroughly explain the utmost important steps under each module. By means of
modules, we will contextualize how zero trust, zero-touch, and reliable end-
to-end chain principles can be addressed. Note that such a trust and
reputation framework has been designed under the 5GZORRO project [13], and in
consequence, a few concepts will be briefly introduced throughout the
following subsections to contextualize and detail the decisions taken.

Figure 1: Design of the proposed reputation-based trust framework

###  III-A Information gathering and sharing module

First and foremost, the trust framework collects raw data from multiple
information sources such as the Data Lake, and resource and service catalog
(see Figure 1). To contextualize, the Data Lake is a centralized and shared
data environment in its native format that leverages big data and the catalog
refers to a decentralized repository employed for identifying available
resource and service. These information sources provision an extremely
fruitful range of data for our trust and reputation framework. It can infer
data like SLA breach predictions related to certain resource and service
providers and statistic parameters with respect to the available product
offers in the distributed marketplace platform, etc.

Depending on how the information is gathered, the TaaS classifies it into
direct and indirect trust. When it comes to direct trust, it is linked to
personal experiences the trustor had after interacting with a trustee. In
particular, our proposal is centered on the reputation deemed through
providers and offer trust histories. Concerning indirect trust, it is
conventionally collected from third-party recommendations. Yet,
recommendations do not always come from trustworthy third-parties. The
proposed trust framework integrates two mechanisms to avoid misleading
recommendations as much as possible. Firstly, a feedback credibility mechanism
is leveraged to figure out the recommender honesty through the Personalized
Similarity Metric (PSM). On another hand, our TaaS also integrates a dynamic
list of trustworthy recommenders, which is originated from previously computed
trust scores. The list is continuously updated after each new interaction and
it also contemplates the time factor to weigh up the most up-to-date scores as
the most relevant. Lastly, it should be highlighted that the information
gathering steps should be applied to each entity involved in the trust chain
and not only to the extremes.

###  III-B Trust computation module

Once all available trust information has been collected, such information is
forwarded to the trust computation module to be processed. It is worth
mentioning that this step is carried out regardless of whether stakeholders
have a previous trust relationship for some time or whether they belong to the
same domain, an intra-domain relationship, since the zero trust principle must
be complied with. In spite of the reputation-based trust framework expects to
support multiple trust assessors such as Bayesian networks and the PowerTrust
model in the foreseeable future (see Figure 1), this manuscript is principally
centered on the PeerTrust model [14]. PeerTrust is based on interactions and
designed for distributed environments in which satisfaction, feedback
credibility, transaction factor, and community factor are the pillars to build
trustworthy establishments. Nevertheless, it only introduces the theoretical
concepts under the above-mentioned pillars described in [14] and not how they
should be formulated. Because of that, we decided to go for the PeerTrust
model as it brings a high flexibility level to adapt the trust model to the
final enforcement scenario as well as meet the distributed philosophy followed
by the 5GZORRO marketplace platform, in which this framework is being
utilized. In this vein, we have designed and developed several pillars to
delineate the final trust scores. Yet, since the stakeholder’s satisfaction is
the utmost importance pillar and the most complex, we have put a special
emphasis on it.

Firstly, the satisfaction represented below in (1) measures the acceptance
degree that a stakeholder $u$ has with another after finishing the i-th
trustworthy interaction. In this regard, we considered two key dimensions to
discover the final satisfaction value, the provider’s satisfaction and the
product offer’s satisfaction. Note that $\psi$ and $\phi$ are the weights of
each dimension and they must satisfy that $\psi+\phi=1$.

$S(u,i)=\psi\cdot PS(u,i)+\phi\cdot PO(u,I)$ (1)

Concerning the provider’s satisfaction, it is composed of three main features,
as shown in (2): the reputation of a stakeholder $j$ (Rep); a set of $n$
recommendations (Rec) about the target $j$ from a trusted third party (TTP);
and the last trust score for each recommender in the previous set. Thence, the
satisfaction of stakeholder $u$ on the i-th interaction will be computed about
the target stakeholder $j$.

$PS(u,j)=Rep(u,j)\cdot\left(\displaystyle\bigoplus_{x=1}^{n}Rec(x,j)\cdot
T^{(t-1)}(u,x)\right)$ (2)

In this sense, Rep represents the average reputation that the stakeholder $u$
has on the stakeholder $j$ contemplating all its available resources and
services. This reputation function calculated below in (3) considers features
such as available assets (AA), total assets (IA), available assets at a given
location (AAL), total assets at a given location (IAL), the total number of
predicted SLA violations (PV) that were lastly managed both successful (MV )
and unsuccessful (EV ), and no-predicted SLA violations (NPV). In addition,
multiple time windows are also deemed together with weighting factors to cater
for higher relevance to the newest scores.

$Rep(u,j)=\displaystyle\sum_{k=1}^{n}\varepsilon(k)\cdot\frac{\bigg{(}\frac{AA(j)}{IA(j)}+\frac{AAL(j)}{IAL(j)}+2\cdot\frac{MV(j)}{PV(j)}-2\cdot\frac{EV(j)+NPV(j)}{PV(j)}\bigg{)}+2}{6}$

(3)

When it comes to the provider’s satisfaction, we additionally leverage an
aggregation operation based on the arithmetic mean to combine recommendations
($Rec$) about the stakeholder $j$ with the last trust score provided by the
recommender. Once the computation of a provider’s satisfaction has been
completed, the next step is to reckon the product offer’s satisfaction. To
deal with it, the reputation-based trust framework utilizes similar dimensions
but adapted to only take into account information about a particular product
offer since the provider’s satisfaction considers all available assets. In the
5GZORRO ecosystem, there are five types of product offers defined in the
resource and service catalog: radio access network (RAN), spectrum,
VNF/container network function (CNF), slice, and edge. Hence, the product
offer’s satisfaction is formed by reputation (Rep), recommendations (Rec), and
last trust scores of a specific type of offer and provider.

With respect to credibility, our approach follows the PSM as it may be applied
to multiple contexts. In particular, such a metric determines how similar two
unfamiliar stakeholders are when evaluating a set of stakeholders. Thereby,
the similarity is the metric leveraged to contrast the opinions about a target
stakeholder as well as measuring the distance of credibility about a set of
stakeholders assessed by both stakeholders. Hence, the higher credibility
distance after evaluating the same set of stakeholder, the less credible the
opinion. Additionally, the reputation-based trust framework introduces two
context factors that allow adjusting the final trust score to the current
transaction type and the community see Figure 1. Firstly, the transaction
context factor pillar intends to forecast a trust value linked to the current
interaction, with a particular stakeholder or product offer, from the number
of feedbacks published in the Data Lake from different time windows. The
transaction context factor rewards stakeholders who publish their interactions
with others in the Data Lake since it spurs future stakeholders to look into
the Data Lake, request recommendations to other stakeholders, and grow the
community. Finally, the community context factor pillar attempts to gather
multiple feedback about a target stakeholder from a dynamic list of
trustworthy recommenders. Hence, the community context factor measures the
number of interactions that a specific stakeholder had in the community
through the contribution of services or resources with other stakeholders.
Besides, multiple recommendations together with the credibility of the
recommender are contemplated through an aggregation function to achieve the
general reputation of the community about a target stakeholder.

In the end, the weighting of the credibility, satisfaction, and transaction
context factor plus the community context factor enables to determine a final
trust score of a target stakeholder by contemplating multiple interactions and
reputations from different recommenders and time windows.

###  III-C Trust storage module

After computing a trust score, the next step is to save both the raw and
inferred data for future establishments and recommendations. To cope with it,
the proposed framework manages two types of information storage sources (see
Figure 1). Because the TaaS is instantiated per domain, a private non-
relational database has been contemplated per instance. The dedicated database
stores principally information with respect to raw data collected from
information sources such as the Data Lake and resource and service catalog,
the PeerTrust model information, and lastly, the trust scores. Furthermore, it
may also save internal policies or rules to be used by the continuous update
module so as to trigger events or detect misbehaviors. Thus, sensitive
information will be stored in the dedicated database as it is not going to be
shared with other stakeholders. In addition to the private database, the
framework also leverages a Data Lake. In particular, the capital aim is to
spread knowledge about trustworthy interactions among stakeholders that form
the 5GZORRO ecosystem. In this regard, newcomers may recognize feasible
recommenders to be consulted. In the same manner, other stakeholders who
already established previous trust interactions across domains may request new
recommendations when their trustworthy recommenders are not able to support
feedback. In contrast to the private database, the Data Lake cannot store
sensitive information since its objective is to be consulted by countless
stakeholders. However, the Data Lake introduces key features such as a long-
term reputation reflection and traceability.

###  III-D Continuous update module

Parallel to the trust storage module, and once a trust score has been
concluded, the trust computation module triggers a continuous update process
focused on the target stakeholder. This module plays a pivotal role as it may
enable earlier identification of plausible attacks through a suitable
configuration of triggers, events, and rules. In this sense, the continuous
update module not only ameliorates the security capabilities of the framework
via context-dependence and dynamicity but also empowers an end-to-end
automatization. Therefore, should unfamiliar phenomenons appear in ongoing
trust relationships, the reputation-based trust framework can identify them
and take the proper decisions.

With regard to real-time events in an established trust relationship, the
continuous update module presents reward and punishments mechanisms to oversee
the stakeholders’ behaviors. Hence, a trust score is recalculated after
appearing new events. Among the principal events contemplated to increase or
dwindle trust scores, we consider security threats, change policy
relationships, SLA violations, service execution failures, and decay of time,
to name but a few. Therefore, whether negative events occur, the previous
trust scores will be diminished applying the proper internal policies, and in
consequence, it could be finished to discover more reliable stakeholders. On
the contrary, favorable events entail an increase in the previous trust score.
Note that the reward and punishment mechanisms also support the zero-touch
principle as it enables to fully automation of the proposed framework. Thence,
human interaction is not required and the reputation-bases trust framework can
smoothly interact with other modules participating in the resource and service
provisioning discovery, as can be observed below.

##  IV Experiments and Results

This section introduces the principal characteristics related to the
5GBarcelona testbed in terms of CPU and memory. Additionally, we showcase
multiple experiments in which the CPU and memory consumption can be observed
for different amount of product offers. Similarly, we can also analyze the
required to run each module of the framework.

###  IV-A Experimental setting

On-demand resource and service allocation to cover user requirements is a real
challenge beyond 5G networks. Therefore, the 5GZORRO project introduces an
innovative solution through a distributed marketplace platform, which enables
a secure and trustworthy provisioning of resource and service capabilities. In
addition to the marketplace, there is a component named Smart Resource and
Service Discovery (SRSD) that enforces zero-touch capabilities and allows
obtaining a set of resources and services through an intent-based discovery.
The SRSD and the trust framework have been integrated to ensure TaaS and elect
the final provider based on its trust scores together with other intents. By
means of such an integration, it is possible to expose reliable telco digital
assets and hire them and enable a zero-touch interaction with other network
orchestration and management components.

In terms of testbeds, the reputation-based trust framework has been deployed
in the 5GBarcelona where the framework was instantiated in a 1vCPU of a 6th
Intel Xeon Gold 5218R with 2.1 GHz and 20 cores. Particularly, the framework
was deployed in a worker with 8 cores and 30 GiB.

###  IV-B Performance Evaluation

To check the proper functionality of the proposed framework, we performed
several experiments.

Firstly, we analyzed the CPU and memory consumption required by our framework.
Thereby, Figure 2 displays an light growth when the number of offers to be
processed increases. In particular, the average CPU consumption of the 1vCPU
(only 1 core) allocated to the framework lies between 0.975 and 0.993 (see
Figure 2a), except for the case of 100 and 200 offers in which are 0.477 and
0.483 respectively. In spite of our vCPU was being used almost 99%, we only
consumed 12% of the total CPU available in the server. In the case of memory,
the framework required around 155 and 195 MiB (only 2% of the total memory) to
process the multiple sets of product offers (see Figure 2b), except for 100
and 200 offers in which the average was set to 140 and 152 MiB. The values
related to 100 and 200 offers were not plotted since they might complicate the
box plot visualization and the number of CPU and memory measurements was lower
than the rest of offers because the framework computed trust scores quicker.

(a) Average CPU consumption per offer

(b) Average memory consumption per offer

Figure 2: Reputation-based trust framework consumption

When it comes to consuming time, the cold-start mechanism doubled the required
time to provide trust scores for each set of offers, as depicted in Figure 3.
Nevertheless, such a mechanism was leveraged because of 5GZORRO ecosystem is
not fully instantiated and there is not information enough about trust
relationships. In the foreseeable future, the cold-start mechanism will be
eradicated and consequently its consumption time. In this regard, the proposed
framework was able to perform the gathering information, computation, and
storage phases in 10.4, 185.6, and 741.4 seconds for 100, 500, and 1000
offers, respectively, in a sequential way.

Figure 3: Reputation-based trust framework consumption time with cold-start

Since the compute phase entailed the higher time, we decided to analyze it in
detail. In Figure 4, we can observe a light exponential increment in which the
credibility consumed more than half of the total time to carry out the three
phases aforementioned, 5.6 out of 10.4 seconds, 104.9 out of 185.6, and 419.9
out of 741.4 for 100, 500, and 1000 offers. Hence, a feasible approach to
address this drawback would be to parallel not only the computation sub-phases
but also the gathering and the storage phases. In this sense, we could execute
different phases of multiple processes in parallel and intend to decrease the
required time to provide final trust scores.

Figure 4: Time distribution by computation phase

From previous experiments, it can be concluded that the reputation-based trust
framework does not require a huge amount of CPU and RAM to request and analyze
information as well as compute trust scores for a high number of offers, for
instance, 500 POs. However, the necessary time to calculate trust scores
should be decreased in next iterations since 180 seconds were consumed to
evaluate the trust of 500 POs. Despite of that, the authors consider the
proposed framework meets with the expected behavior and performance as other
filtering mechanisms, such as intent-based or hardware requirements, are
applied before computing trust scores. Therefore, the PO number to be analyzed
by the proposed framework is normally ranked from 200 and 500.

##  V Conclusion and Future Work

The article at hand analyzes some of the most prominent trust and reputation
approaches in the research field to identify the utilization of network and
business requirements. Taking into account the previous investigations, we
propose a reputation-based trust framework capable of helping stakeholders in
the decision of electing the most reliable providers of resource or service
capabilities available in a distributed marketplace. At the same time, the
proposed framework takes into account critical requirements of 5G multi-party
networks as aforementioned.

In terms of computation, an adapted PeerTrust model has been selected as the
most befitting algorithm to foresee end-to-end trust scores based on
historical interactions and recommendations. Multiple experiments were carried
out in the 5GBarcelona testbed. The outputs showed that the trust and
reputation framework had a slight increase of consumption time when the number
of offers was increased. However, this increase can be reduced by parallelism
techniques.

As future work, we will extend the current functionalities of the framework to
contemplate prominent algorithms such as Bayesian networks and the PowerTrust
model and contrast them with PeerTrust to analyse their performances and
accuracies. In this sense, the TaaS will also be deployed in the 5TONIC
testbed, from Telefonica, to contrast metrics. Besides, additional
functionality to cope with potential trust attacks such as collusion, Sybil,
bad-mouthing, among others, should be designed and developed after looking at
the ones that our trust framework may suffer. Finally, we will analyze
feasible parallelism techniques to be applied in the framework to reduce the
consumption time.

## Acknowledgment

This work has been supported by the European Commission through 5GZORRO
project (grant no. 871533) part of the 5G PPP in Horizon 2020.

## References

  * [1] M. d. Silva and J. Guerreiro, “On the 5g and beyond,” _Applied Sciences_ , vol. 10, no. 20, p. 7091, 2020.
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|  José María Jorquera Valero is a Ph.D. student in Computer Science at Murcia
University. Jorquera Valero received the M.Sc. degree in Computer Science from
the University of Murcia, Spain. His scientific interests include trust,
security, 5G, data privacy, continuous authentication, and cybersecurity.
---|---
|  Pedro Miguel Sánchez Sánchez received the M.Sc. degree in computer science
from the University of Murcia. He is currently pursuing his PhD in computer
science at University of Murcia. His research interests are focused on
continuous authentication, networks, 5G, cybersecurity and the application of
machine learning and deep learning to the previous fields.
---|---
|  Manuel Gil Pérez is Associate Professor in the Department of Information
and Communication Engineering of the University of Murcia, Spain. His
scientific activity is mostly focused on cybersecurity, including intrusion
detection systems, trust and reputation management, and security operations in
highly dynamic scenarios. He received M.Sc. and Ph.D. degrees (the latter with
distinction) in Computer Science from the same University.
---|---
|  Alberto Huertas Celdrán received the M.Sc. and Ph.D. degrees in computer
science from the University of Murcia, Spain. He is currently a postdoctoral
fellow associated with the Communication Systems Group (CSG) at the University
of Zurich UZH. His scientific interests include medical cyber-physical systems
(MCPS), brain–computer interfaces (BCI), cybersecurity, data privacy,
continuous authentication, semantic technology, context-aware systems, and
computer networks.
---|---
|  Gregorio Martínez Pérez is Full Professor in the Department of Information
and Communications Engineering of the University of Murcia, Spain. His
scientific activity is mainly devoted to cybersecurity and networking, also
working on the design and autonomic monitoring of real-time and critical
applications and systems. He is working on different national (14 in the last
decade) and European IST research projects (11 in the last decade) related to
these topics, being Principal Investigator in most of them.
---|---


# Meta Learning in Decentralized Neural Networks:
Towards More General AI

Yuwei Sun1,2

###### Abstract

Meta-learning usually refers to a learning algorithm that learns from other
learning algorithms. The problem of uncertainty in the predictions of neural
networks shows that the world is only partially predictable and a learned
neural network cannot generalize to its ever-changing surrounding
environments. Therefore, the question is how a predictive model can represent
multiple predictions simultaneously. We aim to provide a fundamental
understanding of learning to learn in the contents of Decentralized Neural
Networks (Decentralized NNs) and we believe this is one of the most important
questions and prerequisites to building an autonomous intelligence machine. To
this end, we shall demonstrate several pieces of evidence for tackling the
problems above with Meta Learning in Decentralized NNs. In particular, we will
present three different approaches to building such a decentralized learning
system: (1) learning from many replica neural networks, (2) building the
hierarchy of neural networks for different functions, and (3) leveraging
different modality experts to learn cross-modal representations.

## Progress to Date

Common sense is not just facts but a collection of models of the world. The
global workspace theory (Baars 1988) demonstrated that in the human brain,
multiple neural network models cooperate and compete in solving problems via a
shared feature space for common knowledge sharing, which is called the global
workspace (GW). Within such a learning framework, using different kinds of
metadata about individual neural networks such as measured performance and
learned representations, shows the potential to learn, select, or combine
different learning algorithms to efficiently solve a new task. The learned
knowledge or representations from different neural network areas are leveraged
for reasoning and planning. Therefore, we termed this research direction as
Meta Learning in Decentralized Neural Networks which studies how a meta agent
can solve novel tasks by observing and leveraging the world models built by
these individual neural networks. We present the three different approaches to
building such a decentralized learning system: (1) learning from many replica
neural networks (2) building the hierarchy of neural networks, and (3)
leveraging different modality experts.

### Learning from Many Replica Neural Networks

The proliferation of AI applications is reshaping the contours of the future
knowledge graph of neural networks. Decentralized NNs is the study of
knowledge transfer from different individual neural networks trained on
separate local tasks to a global model. In a learning system comprising many
replica neural networks with similar architecture and functions, the goal is
to learn a global model that can generalize to unseen tasks without large-
scale training (Sun, Ochiai, and Esaki 2021). In particular, we studied two
practical problems in Decentralized NNs, i.e., learning with non-independent
and identically distributed (non-iid) data and multi-domain data.

Notably, non-iid refers to data samples across local models are not from the
same distribution, which hinders the knowledge transfer between local models.
To tackle the non-iid problem, we proposed the Segmented-Federated Learning
(Segmented-FL) (Sun, Ochiai, and Esaki 2020) that employs periodic local model
performance evaluation and learning group segmentation that brings neural
networks training over similar data distributions together. Then, for each
group, we train a different global model by transferring knowledge from the
local models in the group. The global model can only passively observe the
local model performance without access to the local data. We showed that the
proposed method achieved better performance in tackling non-iid data of
intrusion detection tasks compared to the traditional federated learning
(McMahan et al. 2017).

On the other hand, multi-domain refers to data samples across local models are
from different domains with domain-specific features. For example, an
autonomous vehicle that learns to drive in a new city might leverage the
driving data of other cities learned by different vehicles. Since different
cities have different street views and weather conditions, it would be
difficult to directly learn a new model based on the knowledge of the models
trained on multi-domain data. This problem is closely related to multi-source
domain adaptation, which studies the distribution shift in features inherent
to specific domains that bring in negative transfer degrading a model’s
generality to unseen tasks. To this end, we proposed a new domain adaptation
method that reduces feature discrepancy between local models and improves the
global model’s generality to unseen tasks (Sun, Chong, and Ochiai 2022). We
devised two components of embedding matching and global feature disentangler
to align learned features of different local models such that the global model
can learn better-refined domain-invariant features. Moreover, we found that a
simple voting strategy that produces multiple predictions and generates
pseudo-labels based on the consensus of local models could further improve the
global model performance. The results of both image classification tasks and a
natural language sentiment classification task showed that the proposed domain
adaptation method could greatly improve the transfer learning of local models.

### Building the Hierarchy of Neural Networks

Hierarchical neural networks consist of multiple neural networks concreted in
a form of an acyclic graph. An early theory of the global workspace theory
(GWT) (Baars 1988) refers to multiple neural network models cooperating and
competing in solving problems via a shared feature space for common knowledge
sharing. Built upon the GWT, the conscious prior theory (Bengio 2017)
demonstrated the sparse factor graphs in space of high-level semantic
variables and simple mapping between high-level semantic variables. To study
the hierarchy of neural networks, we proposed homogeneous learning for self-
attention decentralized deep learning (Sun and Ochiai 2022a). In particular,
we devised a self-attention mechanism where a local model is selected as the
meta for each training round and leverages reinforcement learning to
recursively update a globally shared learning policy. The meta observes the
states of local models and its surrounding environment, computing the expected
rewards for taking different actions based on the observation. As mentioned in
(Craik 1967), with a model of external reality and an agent’s possible
actions, it can try out various alternatives and conclude which is the best
action using the knowledge of past events. The goal is to learn an optimized
learning policy such that the Decentralized NNs systems can quickly solve a
problem by planning and leveraging different local models’ knowledge more
efficiently. The results showed that the learning of a learning policy greatly
reduced the total training time for an image classification task by 50.8%.

### Leveraging Different Modality Experts

Information in the real world usually comes in different modalities. The
degeneracy (Smith and Gasser 2005) in neural structure refers to any single
function can be carried out by more than one configuration of neural signals
and different neural clusters participate in several different functions.
Intelligence systems build models of the world with different modalities where
spatial concepts are generated via modality models. We demonstrate cross-modal
learning in multimodal models (Radford and et al. 2021). Notably, we studied
the Visual Question Answering (VQA) problem based on self-supervised learning
(Sun and Ochiai 2022b). By leveraging the contrastive learning of different
model components, we aimed to align the modality representations encouraging
the similarity of the relevant component outputs while discouraging the
irrelevant outputs. Such that the learning framework learns better-refined
cross-modal representations for unseen VQA tasks based on the knowledge
learned from different VQA tasks of local models.

## Anticipated Progress

The vast majority of current neural networks lack sophisticated logical
reasoning and action-planning modules. We aim to study a neuro-symbolic
approach to improving the explainability and robustness of knowledge sharing
in the Global Workspace (GW) of Decentralized NNs. Furthermore, we consider
there are several necessary components for building such a neuro-symbolic
learning framework, i.e., causal models and probabilistic Bayesian neural
networks (Hsu 1997), and associative memory like Hopfield Network (Hopfield
1982). In particular, we aim to tackle the tasks of visual grounding such as
visual question answering and image captioning. In this regard, we will
revisit and reintegrate the classical symbolic methods into the decentralized
neural networks theory to improve the hierarchical reasoning of the meta agent
for leveraging different modality expert models. The anticipated contribution
is establishing a new learning framework to perform efficient causal discovery
and inferences based on decentralized neural networks for improving generality
in visual language modeling.

## References

  * Baars (1988) Baars, B. J. 1988.  _A Cognitive Theory of Consciousness_.  Cambridge University Press.
  * Bengio (2017) Bengio, Y. 2017.  The Consciousness Prior.  _arXiv: 1709.08568_.
  * Craik (1967) Craik, K. 1967.  The Nature of Explanation.  In _CUP Archive_.
  * Hopfield (1982) Hopfield, J. 1982.  Neural networks and physical systems with emergent collective computational abilities.  In _Proceedings of the National Academy of Sciences_.
  * Hsu (1997) Hsu, W. H. 1997.  Probabilistic Learning in Bayesian and Stochastic Neural Networks.  In _AAAI_.
  * McMahan et al. (2017) McMahan, B.; Moore, E.; Ramage, D.; and et al. 2017.  Communication-Efficient Learning of Deep Networks from Decentralized Data.  In _AISTATS_.
  * Radford and et al. (2021) Radford, A.; and et al. 2021.  Learning Transferable Visual Models From Natural Language Supervision.  In _ICML_.
  * Smith and Gasser (2005) Smith, L.; and Gasser, M. 2005.  The Development of Embodied Cognition: Six Lessons from Babies.  In _Artif. Life_.
  * Sun, Chong, and Ochiai (2022) Sun, Y.; Chong, N.; and Ochiai, H. 2022.  Multi-Source Domain Adaptation Based on Federated Knowledge Alignment.  In _arXiv:2203.11635_.
  * Sun and Ochiai (2022a) Sun, Y.; and Ochiai, H. 2022a.  Homogeneous Learning: Self-Attention Decentralized Deep Learning.  In _IEEE Access_ , volume 10, 7695–7703.
  * Sun and Ochiai (2022b) Sun, Y.; and Ochiai, H. 2022b.  UniCon: Unidirectional Split Learning with Contrastive Loss for Visual Question Answering.  In _arXiv:2208.11435_.
  * Sun, Ochiai, and Esaki (2020) Sun, Y.; Ochiai, H.; and Esaki, H. 2020.  Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs.  In _IJCNN_.
  * Sun, Ochiai, and Esaki (2021) Sun, Y.; Ochiai, H.; and Esaki, H. 2021.  Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness.  In _IEEE Transactions on Artificial Intelligence_.