# Digital Ethics in Federated Learning

Liangqi Yuan, , Ziran Wang, , and Christopher G. Brinton Manuscript received
August 31, 2023.L. Yuan, Z. Wang, and C. G. Brinton are with the College of
Engineering, Purdue University, West Lafayette, IN 47907, USA (e-mails:
<EMAIL_ADDRESS><EMAIL_ADDRESS>cgb@purdue.edu).

###### Abstract

The Internet of Things (IoT) consistently generates vast amounts of data,
sparking increasing concern over the protection of data privacy and the
limitation of data misuse. Federated learning (FL) facilitates collaborative
capabilities among multiple parties by sharing machine learning (ML) model
parameters instead of raw user data, and it has recently gained significant
attention for its potential in privacy preservation and learning efficiency
enhancement. In this paper, we highlight the digital ethics concerns that
arise when human-centric devices serve as clients in FL. More specifically,
challenges of game dynamics, fairness, incentive, and continuity arise in FL
due to differences in perspectives and objectives between clients and the
server. We analyze these challenges and their solutions from the perspectives
of both the client and the server, and through the viewpoints of centralized
and decentralized FL. Finally, we explore the opportunities in FL for human-
centric IoT as directions for future development.

##  I Introduction

The Internet of Things (IoT) encompasses a phenomenon where physical devices,
embedded with sensors, interact with their surroundings and engage in data
exchange with other devices and systems via the Internet. These devices cover
a vast range, from compact thermostats to large-scale industrial machinery,
all interconnected within the IoT infrastructure. The human-centric IoT
applications focus on devices within the IoT ecosystem that are designed to
focus on human interaction or significantly influenced by human factors [1],
such as smartphones, wearable devices, vehicles, and healthcare appliances.
These devices, through their diverse sensors, incessantly produce a wealth of
highly sensitive data. For example, images taken by smartphones can contain
global positioning systems (GPS) location information, smartwatches can detect
a user’s electrocardiogram (ECG), and vehicle navigation systems document a
driver’s routes. Certain companies might require customers to disclose such
rich personal data to improve their machine learning (ML) models. The growing
demand for data invariably raises concerns over potential privacy breaches and
misuse of associated data, introducing pressing digital ethics issues in our
interconnected digital era, such as privacy, security, and fairness.

Federated learning (FL) [2] represents a decentralized learning paradigm,
designed to facilitate multi-party collaboration while safeguarding user
privacy. Its essence lies in sharing ML models rather than raw user data,
achieving privacy preservation. Moreover, with the exponential growth of users
and their data, the transmission and storage of vast amounts of raw data pose
significant challenges for communication channels and server storage. FL can
also be perceived as a form of knowledge distillation, distilling knowledge
from raw data into model parameters to alleviate communication overhead. In
line with the presence or absence of a server for coordination, management,
and aggregation, FL can be categorized into two frameworks: centralized FL
(CFL) and decentralized FL (DFL) [3]. Initially proposed by Google researchers
and deployed in the Google keyboard for cooperative learning in keyboard input
recommendation models [4], FL has found extensive applications in numerous
sectors, such as healthcare, mobile services, and intelligent transportation
systems, facilitating collaboration amongst widely distributed edge devices or
institutions.

Figure 1: The human-centric Internet of Things (IoT) applications within the
(a) centralized federated learning (CFL) and (b) decentralized federated
learning (DFL) frameworks.

FL presents a powerful approach for mitigating privacy concerns inherent in
collaborative ML. However, digital ethical concerns extending beyond privacy
are often overlooked [5], especially within the context of the human-centric
IoT. Notably, most existing research inadequately addresses ethical
considerations from the narrow perspective of the client side. For example,
users of applications like Google Keyboard may remain oblivious to or
unconcerned about the underlying FL algorithms. Their primary concern is that
they are contributing their model but not receiving highly accurate
personalized recommendations in return. This disparity in expectations can
breed disappointment and potentially lead to a discontinuation of use.
Consequently, human emotions may emerge as a vital factor in ensuring the
continuity and longevity of FL frameworks in these contexts.

In this paper, we present a discourse on the digital ethical issues arising
within both CFL and DFL deployments in human-centric IoT applications, as
depicted in Fig. 1. We illustrate the FL lifeline in Fig. 2, encompassing two
trajectories, namely human-centric IoT and the digital ethics of FL. Apart
from user privacy, people are generally concerned about fairness,
interpretability, accountability, transparency, and other aspects.
Additionally, issues related to user management, incentives, penalties,
continuity, and compatibility with new users are important considerations in
FL systems. In addition to pursuing higher performance and convergence in ML
and optimizing communication networks, there is also a growing interest in the
social, psychological, and economic aspects of FL, among others.

The organization of this paper is as follows: First, we provide an in-depth
examination of the definitions and perspectives of clients and the server, as
well as the underlying reasons for the game dynamic relationship that arises
between the CFL and DFL frameworks (Sec. II). The discrepancies, limitations,
and information asymmetry between clients and the server, especially the
fundamental difference in their objectives, inevitably give rise to a game
dynamic (Sec. III). Subsequently, the resultant trust issues emerging from
divergent objectives appear specifically as client skepticism towards the
fairness of the FL framework (Sec. IV). Notably, the difference in perspective
also leads to varied definitions of fairness between clients and the server.
Adjacent to the issue of fairness is the problem of incentive mechanisms for
clients (Sec. V). Beyond the extensively researched server-led incentive
mechanisms, we discuss the potential for a reputation system, established by
the client community, to become one of the primary mechanisms in DFL.
Alongside fairness and incentives, we also touch upon the continuity of FL’s
development and updates (Sec. VI). Based on these four properties (i.e., game
dynamics, fairness, incentives, and continuity), we proceed to discuss
opportunities to foster the continuous, active, and positive development of FL
(Sec. VII). Finally, we draw conclusions from this paper (Sec. VIII).

Figure 2: Lifeline of digital ethics and the human-centric Internet of Things
(IoT) applications in federated learning (FL).

##  II Variance of Perspectives

Within FL, different roles possess distinct perspectives and varied levels of
knowledge. The core of the game dynamics in FL stems from the differences in
clients’ contributions (e.g., the volume of raw data), the learning process,
and the information asymmetry among participants.

###  II-A Omniscient (Authors’ and Readers’) Perspective

Currently, a significant portion of research papers on FL tends to overlook
the information asymmetry between clients and servers. They often adopt an
idealized perspective, optimizing FL based on the assumption of complete and
perfect knowledge. These design methods, founded on the notion of omniscient
information, fail to address the practical challenges that arise from limited
information exchange, client data heterogeneity, and potential trust issues.
Recognizing and considering the scenarios of information asymmetry are crucial
for developing effective FL systems.

###  II-B Server’s Perspective

In CFL, the role of the server is to receive model parameters from all
clients, aggregate them, and then redistribute the aggregated model. The
server, however, remains oblivious to how clients collect data, train models,
and handle the post-processing of models. Some CFL frameworks make
presumptions that the server is privy to more extensive metadata from clients.
For example, in the case of FedAvg, each client not only sends their model
parameters but also transmits the volume of their local raw data. This
additional metadata allows the server to perform weighted averaging.
Therefore, in CFL, the resources or perspectives available to the server can
be summarized as previously aggregated models, current and past models from
clients, and other metadata that clients are asked to send, such as volume of
raw data, performance on local test sets, losses, training epochs, etc.

###  II-C Clients’ Perspective

Considering the perspective of the clients, we discuss the contexts of both
CFL and DFL frameworks, as illustrated in Fig. 1.

In CFL, clients are oblivious to each other’s information, such as the number
of clients, the volume of raw data each client holds, the learning process,
and the model performance of each. In specific FL scenarios, for example, when
healthcare institutions act as clients for FL, the components such as
optimizers, loss functions, learning rates, and training epochs differ from
client to client. Additionally, clients lack knowledge about the server-side
details, like the aggregation method employed by the server. Hence, in a CFL
framework, the only available information for each client is the aggregated
model received from the server.

In DFL, clients directly share models without the coordination of a server.
For example, in a fully connected network topology, every client within the
DFL framework needs to transmit their own model parameters to all other
clients, and reciprocally receive models from them. As a result, a certain
framework, such as network topology, communication direction, frequency, and
so forth, needs to be agreed upon among clients. They also need to be
cognizant of certain information about other clients, like their addresses and
ports. Additionally, some extra metadata, such as the volume of raw data,
number of clients, model versions, etc., might also be transmitted as per the
requirement. Therefore, in DFL, for the system to function correctly, clients
need to establish a communication protocol among themselves and are required
to directly disclose their local information to other clients.

TABLE I: Definition of Digital Ethics for Client and Server in CFL and DFL |  Perspective |  Game Dynamics (Objective) |  Fairness |  Incentives |  Continuity
---|---|---|---|---|---
Centralized Federated Learning
Omniscient |  Everything |  Generalized and personalized model |  Fairness-aware strategy |  Incentive mechanism design |  Server Management Enhancement
Server |  Models and information from the clients |  Generalized model |  Overall accuracy is highest |  Revenue, market share, available generalized models |  Management of clients and models
Client |  Model from the server |  Personalized model |  Local accuracy is highest |  Rewards, punishments, and model updates |  Compliance with server management
Decentralized Federated Learning
Omniscient |  Everything |  Generalized and personalized model |  Fairness-aware strategy |  Incentive mechanism design |  Encouraging spontaneous management by clients
Server | N/A
Client |  Model from other clients |  Personalized model |  Local accuracy is highest |  Exposure, reputation, and model updates |  Identify, accuse, and report malicious clients

##  III Game Dynamics

###  III-A Why Game Dynamic Emerge?

Compared with distributed learning that assigns tasks to nodes or miners, FL
inherently emphasizes more on the data-generating clients. Governed by these
data-holding clients, and propelled by self-interest and greed, the
inclination towards selfish behavior and a lack of trust in others could
surface. This drives the dynamics of interaction among clients in a game
dynamics context, where each seeks to maximize personal gains.

This dynamic is primarily attributed to significant data heterogeneity among
clients, where the server-aggregated model may not exhibit exceptional
performance on all clients. Firstly, inter-group heterogeneity exists among
clients. For example, professionals such as professors, doctors, and lawyers
using Google Keyboard would require highly tailored recommendations due to
their distinctive fields of expertise. Secondly, intra-group heterogeneity
exists within each group of users, wherein each user’s academic discipline,
level of knowledge, years of expertise, and other factors vary. Lastly, system
heterogeneity among clients arises from variations in IoT devices, which
includes disparities among sensor and instrument manufacturers, differences in
software versions of devices, and varying user operations.

###  III-B Game Dynamics between Client and Server

In CFL, clients and the server share similar yet fundamentally different
objectives: clients aim to achieve the best-performing personalized model on
their local dataset, while the server seeks to achieve the best average-
performing general model across all clients. While this setup can be mutually
beneficial, a game dynamics relationship emerges between the clients and the
server due to the trade-off between personalized performance and
generalization.

Personalized FL represents a potential solution to mitigating the game
dynamics between clients and the server, as it seeks to satisfy the objectives
of both parties [6]. There are two main strategies in this context: client
compromise and server compromise. In the case of client compromise, a
simplistic implementation would involve the client performing additional
gradient descent upon receiving the generalized global model (i.e., meta-
learning), thus achieving personalized expansion [7]. Conversely, in server
compromise, a common practice is clustered FL. In this scenario, the server
can create multiple aggregated global models based on the nature of the
clients or clustering of client models, with even the potential for multiple
servers to partition different regions for aggregation (i.e., hierarchical
FL). Regardless of whether it is client compromise or server compromise, these
strategies both entail additional overheads, such as computation,
communication, and storage.

###  III-C Game Dynamics among Clients

In DFL, despite the symmetric roles of clients in communication and knowledge
propagation, competition can still emerge due to data heterogeneity and system
heterogeneity. They all share the same but conflicting objective of striving
for the best model performance on their respective local data sets. However,
given the data heterogeneity among clients, it is more common that the models
from other clients perform poorly on their local dataset [8].

Analogous to the two compromise strategies in CFL, DFL also incorporates
similar personalized methods to enhance the performance of aggregated models
on clients’ local data sets. Apart from model post-processing methods such as
meta-learning, DFL can reduce data heterogeneity among clients within a
cluster by establishing different topological structures, mimicking the
clustered FL. In particular, in real-world DFL scenarios, clients might prefer
freely forming their clusters and establishing DFL network topologies among
similar and familiar populations, such as city clusters and suburb clusters
determined by geographical locations. These more flexible and customizable
network topologies, although more challenging to establish initially, also
confer more personalized and trustworthy DFL with communication cost
advantages.

TABLE II: State-of-the-art technologies, strategies, and mechanisms Issue |  Definition |  Technologies, strategies, and mechanisms
---|---|---
Game Dynamics (Objective) |  Divergent objectives amongst clients and server |  • Personalized FL – Server compromise (e.g., clustering, knowledge distillation) – Client compromise (e.g., meta-learning, data augmentation)
Fairness |  Contribution and performance distribution among clients |  • Averaging (i.e., arithmetic mean) • Weighted averaging (e.g., FedAvg is based on sample volume) • Post-processing (e.g., personalized CFL)
Incentives |  Clients contribute honestly, actively, and positively |  • Feedback (e.g., aggregated model) • Reward (e.g., sponsorship, subscription) • Reputation (e.g., like, follow, share)
Continuity |  FL maintains its operations and efficiency to prolong its lifecycle |  • Enlistment of new clients • Rapid and efficient model iteration • Low computational, communication, and storage overheads

##  IV Fairness

###  IV-A How to Define Fairness?

A fairer system would enhance client trust, incentivize client contributions,
reduce the potential for free-riding behaviors, attract more new client
engagement, and bolster the long-term continuity of the system, among other
benefits. Fairness has always been a central theme in human cooperation, and
FL is no exception. Particularly in CFL, the method of aggregation has spurred
discussions concerning fairness. The question of fairness in FL is indeed
become a focal point of discourse. It involves considering whether the FL
framework should prioritize the majority of users and clients with a larger
number of samples, or whether it should also take into account clients with
fewer samples that may have lower representativeness [9]. Furthermore, the
perspectives of both clients and the server may also sway their
interpretations of fairness. Clients may not have full visibility into the
server’s aggregation algorithm, nor comprehend the performance of the
aggregated model across different clients.

###  IV-B Fairness of Server

From the perspective of the server, its objective is to pursue a generalized
model that maximizes the overall average performance of all clients. Driven by
this objective and the pursuit of generalization in FL, the server’s concept
of fairness often tends to favor clients with a greater influence or voice. In
the classic FedAvg algorithm, the server conducts a weighted averaging
aggregation based on the sample number of each client. This approach seems
fair because the sample size can extent reflect the performance and
credibility of the model to some extent, and can be seen as a reward for
clients with more samples, as the aggregated model is more likely to bias
towards them. However, for those underrepresented clients, the performance of
the aggregated model might be unsatisfactory. Furthermore, the dominance of
large sample clients could lead to low sample diversity and cause the
aggregated model to lose its generalization capability. On the contrary, an FL
framework involving non-weighted averaging during aggregation might demotivate
clients with large sample sizes, subsequently diminishing system performance
and continuity. Hence, from the server’s standpoint, the conception of
fairness remains a topic open to debate.

###  IV-C Fairness of Client

Conversely, from the client’s perspective, their interpretation of fairness
tends to be simpler. This is primarily due to the likelihood that they are
either unaware of or unconcerned with the server’s aggregation algorithm and
the performance of the aggregated model on other clients. Hence, within the FL
framework, clients would consider the system fair from a standpoint of
individual fairness, provided the model maintains acceptable performance
locally. A noteworthy example is Google Keyboard, where users contribute local
model parameters in their usage, and in return, benefit from personalized
recommendations. Interestingly, these recommendations from Google Keyboard are
not necessarily completely accurate. As long as the output is within the
user’s range of acceptability, the application can maintain its advantage
relative to non-personalized keyboard applications. Of course, it is crucial
to note that the level of acceptable performance may vary according to
individual clients’ requirements and should not be generalized. When using
Google Keyboard, users are often oblivious to or indifferent towards the
server’s aggregation algorithm and have minimal or no knowledge of the model’s
performance among other users. Users are likely to be self-interested,
prioritizing their user experience without considering any factors related to
others, i.e., a non-cooperative game scenario.

##  V Incentives

###  V-A Incentives Driven by Server

As the owner, leader, and manager of FL frameworks, the server typically
aspires for its framework to undergo large-scale, active, positive, and
continuable development. Thus, how the server employs incentive strategies to
encourage clients to report their models, metadata, contributions, and even
flaws in a rational, honest, and proactive manner remains an unresolved issue.
The right to use the aggregated model itself serves as a form of reward
(passive incentive), and current incentive strategies also contemplate
offering additional rewards disseminated by the server to motivate clients
(active incentives). The client contributions in these incentive strategies
can follow economic principles, such as game theory, auction, contract,
matching theory, and so forth [10]. The Stackelberg game, in particular, has
garnered considerable attention due to its alignment with the behaviors of the
server and clients in a CFL setting. Besides these active incentives, punitive
incentives may also serve as a potential strategy. For example, the right to
use the aggregated model could be revoked if a client’s contribution does not
meet expectations.

###  V-B Incentives Driven by Client Community

Within the context of DFL, the absence of server coordination and the
customizability of diverse network topologies render the incentive problem
more variable and challenging [11]. On the one hand, there is no server to
generate and distribute rewards, while on the other hand, calculating client
contributions is especially difficult due to mutual distrust among clients.
Therefore, certain passive incentive strategies may become more effective and
prevalent than active incentives. On one side, clients can acquire the right
to use the models by participating in the DFL community. Simultaneously, due
to the factors of information asymmetry and mutual invisibility of information
among clients, they are unaware of the size of each other’s contributions,
such as the volume of raw data, training epochs, optimization results, etc.
Consequently, they might be more inclined to share models imbued with local
knowledge in exchange for other clients’ model updates. The motivation here is
to garner as many resources as possible from the client community, albeit at
the expense of disclosing local resources. We can draw inspiration from
altruistic contribution behaviors observed in human societies, such as open-
sourcing on Github, answering questions on Stack Overflow, voluntarily
performing peer reviews, etc. While free-riding attacks (where some users
garner knowledge from others without contributing themselves) are inevitable,
the influence of reputation and prestige can nonetheless maintain a virtuous
cycle within the community [12].

##  VI Continuity

Continuity is a critical feature for the survival, revenue generation, and
expansion of any application, system, or framework. In terms of FL, continuity
signifies the pause, elimination, and reactivation of inactive clients, the
continual, active, and voluntary updates from current clients, and the
willingness, eligibility, and data diversity of a large number of prospective
clients.

###  VI-A Continuable Development of Server

From the server’s perspective, continuable development necessitates addressing
and responding to the needs of these three classes of clients - inactive,
current, and potential - while also considering the maintenance of different
versions of the model to prevent catastrophic forgetting. More specifically,
due to the continuous generation of new data by clients in the real world,
particularly IoT devices, the local model updates of clients are typically
based on the latest data. Although new models are evidently more compelling
due to factors such as scenario updates, user utilization, and concept drift,
old versions of the models do not entirely lose their contributions. A
potential example could be an application using IoT devices, such as a
smartwatch that monitors user’s ECG patterns. The ECG readings of users are
likely to differ between weekdays and weekends, thus the models derived from
weekend data might warrant individual storage. In practice for FL, while the
server is aggregating the current versions of local models, it also
incorporates previous versions with appropriate weighting. Furthermore,
clients are granted the ability to trace back and retrieve prior versions of
the model at any time. This feature serves as a safeguard against potential
instability in client performance due to model updates.

###  VI-B Continuable Update of Client

In the context of CFL, clients strive for long-term stability, rapid
iterations, and efficient updates of the aggregated models from the server.
Thus, they may work hard to deploy server-updated models at the earliest
opportunity to achieve enhanced performance and user experience. Beyond their
expectations from the server, under rational circumstances, clients might also
attempt to report their model parameters to the server as rapidly, thoroughly,
and accurately as possible, to ensure their models are significantly
considered during the server’s aggregation process. This is because the server
cannot indefinitely wait for all clients to upload their models. Therefore, in
a rational state, the behavior of client updates is balanced between the long-
term nature of data collection and the rapidity of model updates.

In the scenario of DFL, clients within the community may voluntarily identify,
denounce, and report malicious clients performing adversarial attacks (e.g.,
model poisoning) in order to protect the community, given that this relates to
their own interests. This is because the incorporation of models from these
malicious clients into the FL process could potentially harm their interests.
Clients might also proactively share their models with other clients,
establishing a good reputation, so that other clients will be inclined to
promptly share their model updates in return. One potential concern is that
the DFL client population may exhibit exclusionary tendencies. Specifically,
the mistrust towards new clients and the uncertainty brought about by their
models, especially within smaller communities, can be quite pronounced. This
may further hinder the continuity and growth of such small-scale communities.

##  VII Opportunities

###  VII-A Interplay of Game Dynamics, Fairness, Incentives, and Continuity

The issues of game dynamics, fairness, incentive mechanisms, and continuity in
FL are interrelated and mutually impactful. For example, if an FL framework
could perfectly achieve the objectives of all clients, such as Google Keyboard
ideally meeting user expectations, users would naturally diminish their
concerns about fairness. A fairness-aware strategy can also be considered as
an incentive mechanism where clients contributing more are rewarded
proportionally. Taking FedAvg as an example, clients might make great efforts
to contribute as much local data as possible to the model training, to gain a
more significant voice during the server’s model aggregation process.
Therefore, fairness-aware strategies of weighted aggregation indirectly
incentivize clients to make more contributions. Concurrently, this enhances
the continuity of the FL framework, as each client will make an effort to
collect data, train models, and participate in FL updates promptly to gain
rewards. Under such continuable conditions, the game dynamics within the FL
framework are also mitigated, as each client generates a steady stream of data
resources, enabling the training of more robust models. Therefore, for the
issues of game dynamics, fairness, incentive mechanisms, and continuity, both
parallel multi-solution approaches and single-solution breakthroughs are
viable options.

###  VII-B Integration with Sociology and Ethology

FL essentially represents a form of knowledge propagation, a method that is
already widespread, diverse, and matured within both human societies and
animal behaviors [13]. For example, the instructive paradigm between a teacher
and students can offer insights to CFL, resonating with the architecture of a
large model within the server and smaller models among clients utilized in
federated knowledge distillation [14]. Intriguingly, a similar hierarchical
structure is observed in the field of ethology, particularly within ant
colonies or bee hives. Here, directives (models) from the queen ant or queen
bee (server) are disseminated to the worker ants or bees (clients), offering a
clear instance of role distribution.

DFL is increasingly becoming a focus for researchers, due to its capacity to
circumvent limitations imposed by server dependency, and also its reflection
of more prevalent modes of knowledge dissemination among clients within human
societies and ethology. For example, in the context of conferences, speakers
(clients) present their research findings (models) to all attendees (other
clients), which can be viewed as a manifestation of fully connected DFL. In
group collaborations, each team member (client) contributes a part towards a
common goal (model), mirroring the concept of split DFL. Interestingly,
similar decentralized patterns of knowledge dissemination are observable in
animal behavior. For example, within a school of fish, individual fish
(clients) only communicate with their neighbors (gossip protocol), but when
danger arises, the alert signal (model) spreads across the entire school
(other clients), promoting swift collective evasion [15].

Therefore, incorporating insights from sociology and ethology can effectively
enhance FL organizational structures that are centered on IoT users, better
aligning with the psychological expectations of users as clients.

###  VII-C Deployment Optimization in Federated Learning

Current research mainly centers on the optimization of training and
communication within FL, largely overlooking the strategy and timing for
deploying the base model in FL on client devices. Specifically, in classical
algorithms such as FedAvg, the fundamental operational cycle entails download
$\rightarrow$ train $\rightarrow$ upload $\rightarrow$ download $\rightarrow$
deploy. In contrast, personalized algorithms, such as meta-learning, follow a
cycle of download $\rightarrow$ train $\rightarrow$ upload $\rightarrow$
download $\rightarrow$ train $\rightarrow$ deploy. Therefore, it’s evident
that the deployment sequence within the communication and training processes
significantly affects the performance of the model. With this in mind, we
propose considering two distinct deployment sequences, facilitating the
deployment of either generalized or personalized models, contingent on the
specific use case:

  1. (i)

Deploy post-download for a generalized model: The model deployed is the
aggregated one, offering wider generalization capabilities. However, it may
not necessarily deliver optimal performance on local datasets.

  2. (ii)

Deploy post-training for a personalized model: The model deployed is the one
locally trained on the aggregated model, offering a higher degree of
personalization and subsequently, enhancing confidence in the model’s
performance.

Beyond the influence of the order of deployment on performance within the FL
process, in the real world, due to the sequential and time-sensitive nature of
data collection from IoT devices, excessive waiting for responses from the
server or other clients may degrade model performance. Therefore, deployment
optimization in FL, as an issue rooted in real-world applications, builds upon
the foundational capacities of training and communication to further enhance
FL’s performance, credibility, and operational efficiency.

##  VIII Conclusion

In this paper, we explore and discuss FL in the context of human-centric IoT
applications, with a particular emphasis on the advancements made by FL
algorithms in addressing human privacy concerns, as well as other digital
ethical dilemmas. We take into account perspectives from three distinct roles:
the omniscient, clients, and the server, with a detailed analysis of both the
CFL and DFL frameworks. Each of these roles, characterized by varying
objectives and information asymmetries, raises game dynamics and trust crises,
which in turn incite debates around fairness, incentive, and continuity. This
paper aims to highlight the prevalent disregard for human digital ethics in
the current FL paradigm and to inspire the future design of FL frameworks from
sociological, psychological, and economic perspectives.

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