2,454 research outputs found
Towards Fairness-Aware Federated Learning
Recent advances in Federated Learning (FL) have brought large-scale
collaborative machine learning opportunities for massively distributed clients
with performance and data privacy guarantees. However, most current works focus
on the interest of the central controller in FL,and overlook the interests of
the FL clients. This may result in unfair treatment of clients which
discourages them from actively participating in the learning process and
damages the sustainability of the FL ecosystem. Therefore, the topic of
ensuring fairness in FL is attracting a great deal of research interest. In
recent years, diverse Fairness-Aware FL (FAFL) approaches have been proposed in
an effort to achieve fairness in FL from different perspectives. However, there
is no comprehensive survey which helps readers gain insight into this
interdisciplinary field. This paper aims to provide such a survey. By examining
the fundamental and simplifying assumptions, as well as the notions of fairness
adopted by existing literature in this field, we propose a taxonomy of FAFL
approaches covering major steps in FL, including client selection,
optimization, contribution evaluation and incentive distribution. In addition,
we discuss the main metrics for experimentally evaluating the performance of
FAFL approaches, and suggest promising future research directions towards
fairness-aware federated learning.Comment: 16 pages, 4 figure
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
Fairness and Privacy in Federated Learning and Their Implications in Healthcare
Currently, many contexts exist where distributed learning is difficult or
otherwise constrained by security and communication limitations. One common
domain where this is a consideration is in Healthcare where data is often
governed by data-use-ordinances like HIPAA. On the other hand, larger sample
sizes and shared data models are necessary to allow models to better generalize
on account of the potential for more variability and balancing underrepresented
classes. Federated learning is a type of distributed learning model that allows
data to be trained in a decentralized manner. This, in turn, addresses data
security, privacy, and vulnerability considerations as data itself is not
shared across a given learning network nodes. Three main challenges to
federated learning include node data is not independent and identically
distributed (iid), clients requiring high levels of communication overhead
between peers, and there is the heterogeneity of different clients within a
network with respect to dataset bias and size. As the field has grown, the
notion of fairness in federated learning has also been introduced through novel
implementations. Fairness approaches differ from the standard form of federated
learning and also have distinct challenges and considerations for the
healthcare domain. This paper endeavors to outline the typical lifecycle of
fair federated learning in research as well as provide an updated taxonomy to
account for the current state of fairness in implementations. Lastly, this
paper provides added insight into the implications and challenges of
implementing and supporting fairness in federated learning in the healthcare
domain
FAC-fed: Federated adaptation for fairness and concept drift aware stream classification
Federated learning is an emerging collaborative learning paradigm of Machine learning involving distributed and heterogeneous clients. Enormous collections of continuously arriving heterogeneous data residing on distributed clients require federated adaptation of efficient mining algorithms to enable fair and high-quality predictions with privacy guarantees and minimal response delay. In this context, we propose a federated adaptation that mitigates discrimination embedded in the streaming data while handling concept drifts (FAC-Fed). We present a novel adaptive data augmentation method that mitigates client-side discrimination embedded in the data during optimization, resulting in an optimized and fair centralized server. Extensive experiments on a set of publicly available streaming and static datasets confirm the effectiveness of the proposed method. To the best of our knowledge, this work is the first attempt towards fairness-aware federated adaptation for stream classification, therefore, to prove the superiority of our proposed method over state-of-the-art, we compare the centralized version of our proposed method with three centralized stream classification baseline models (FABBOO, FAHT, CSMOTE). The experimental results show that our method outperforms the current methods in terms of both discrimination mitigation and predictive performance
A Snapshot of the Frontiers of Client Selection in Federated Learning
Federated learning (FL) has been proposed as a privacy-preserving approach in
distributed machine learning. A federated learning architecture consists of a
central server and a number of clients that have access to private, potentially
sensitive data. Clients are able to keep their data in their local machines and
only share their locally trained model's parameters with a central server that
manages the collaborative learning process. FL has delivered promising results
in real-life scenarios, such as healthcare, energy, and finance. However, when
the number of participating clients is large, the overhead of managing the
clients slows down the learning. Thus, client selection has been introduced as
a strategy to limit the number of communicating parties at every step of the
process. Since the early na\"{i}ve random selection of clients, several client
selection methods have been proposed in the literature. Unfortunately, given
that this is an emergent field, there is a lack of a taxonomy of client
selection methods, making it hard to compare approaches. In this paper, we
propose a taxonomy of client selection in Federated Learning that enables us to
shed light on current progress in the field and identify potential areas of
future research in this promising area of machine learning.Comment: 17 pages, 3 figures, 1 appendix, submitted to TML
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Home appliance manufacturers strive to obtain feedback from users to improve
their products and services to build a smart home system. To help manufacturers
develop a smart home system, we design a federated learning (FL) system
leveraging the reputation mechanism to assist home appliance manufacturers to
train a machine learning model based on customers' data. Then, manufacturers
can predict customers' requirements and consumption behaviors in the future.
The working flow of the system includes two stages: in the first stage,
customers train the initial model provided by the manufacturer using both the
mobile phone and the mobile edge computing (MEC) server. Customers collect data
from various home appliances using phones, and then they download and train the
initial model with their local data. After deriving local models, customers
sign on their models and send them to the blockchain. In case customers or
manufacturers are malicious, we use the blockchain to replace the centralized
aggregator in the traditional FL system. Since records on the blockchain are
untampered, malicious customers or manufacturers' activities are traceable. In
the second stage, manufacturers select customers or organizations as miners for
calculating the averaged model using received models from customers. By the end
of the crowdsourcing task, one of the miners, who is selected as the temporary
leader, uploads the model to the blockchain. To protect customers' privacy and
improve the test accuracy, we enforce differential privacy on the extracted
features and propose a new normalization technique. We experimentally
demonstrate that our normalization technique outperforms batch normalization
when features are under differential privacy protection. In addition, to
attract more customers to participate in the crowdsourcing FL task, we design
an incentive mechanism to award participants.Comment: This paper appears in IEEE Internet of Things Journal (IoT-J
PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning
Facilitated by mobile edge computing, client-edge-cloud hierarchical
federated learning (HFL) enables communication-efficient model training in a
widespread area but also incurs additional security and privacy challenges from
intermediate model aggregations and remains the single point of failure issue.
To tackle these challenges, we propose a blockchain-based HFL (BHFL) system
that operates a permissioned blockchain among edge servers for model
aggregation without the need for a centralized cloud server. The employment of
blockchain, however, introduces additional overhead. To enable a compact and
efficient workflow, we design a novel lightweight consensus algorithm, named
Proof of Federated Edge Learning (PoFEL), to recycle the energy consumed for
local model training. Specifically, the leader node is selected by evaluating
the intermediate FEL models from all edge servers instead of other
energy-wasting but meaningless calculations. This design thus improves the
system efficiency compared with traditional BHFL frameworks. To prevent model
plagiarism and bribery voting during the consensus process, we propose
Hash-based Commitment and Digital Signature (HCDS) and Bayesian Truth
Serum-based Voting (BTSV) schemes. Finally, we devise an incentive mechanism to
motivate continuous contributions from clients to the learning task.
Experimental results demonstrate that our proposed BHFL system with the
corresponding consensus protocol and incentive mechanism achieves
effectiveness, low computational cost, and fairness
Fairness-Aware Client Selection for Federated Learning
Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients)
to train machine learning models collaboratively without revealing private
data. Since the FL server can only engage a limited number of clients in each
training round, FL client selection has become an important research problem.
Existing approaches generally focus on either enhancing FL model performance or
enhancing the fair treatment of FL clients. The problem of balancing
performance and fairness considerations when selecting FL clients remains open.
To address this problem, we propose the Fairness-aware Federated Client
Selection (FairFedCS) approach. Based on Lyapunov optimization, it dynamically
adjusts FL clients' selection probabilities by jointly considering their
reputations, times of participation in FL tasks and contributions to the
resulting model performance. By not using threshold-based reputation filtering,
it provides FL clients with opportunities to redeem their reputations after a
perceived poor performance, thereby further enhancing fair client treatment.
Extensive experiments based on real-world multimedia datasets show that
FairFedCS achieves 19.6% higher fairness and 0.73% higher test accuracy on
average than the best-performing state-of-the-art approach.Comment: Accepted by ICME 202
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