Reputation-Aware Federated Learning Client Selection Based on Stochastic Integer Programming

Federated Learning(FL) has attracted wide research interest due to its potential in building machine learning models while preserving users' data privacy. However, due to the distributive nature of FL, it is vulnerable to misbehavior from participating worker nodes. Thus, it is important to sel...

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Bibliographic Details
Published in:IEEE transactions on big data Vol. 10; no. 6; pp. 953 - 964
Main Authors: Tan, Xavier, Ng, Wei Chong, Lim, Wei Yang Bryan, Xiong, Zehui, Niyato, Dusit, Yu, Han
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Biological system modeling
client selection
Clients
Computational modeling
Costs
Data models
Federated learning
Integer programming
Machine learning
reputation
stochastic integer programming
Stochastic processes
Training
Uncertainty
ISSN:2332-7790, 2372-2096
Online Access:Get full text
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Summary:Federated Learning(FL) has attracted wide research interest due to its potential in building machine learning models while preserving users' data privacy. However, due to the distributive nature of FL, it is vulnerable to misbehavior from participating worker nodes. Thus, it is important to select clients to participate in FL. Recent studies on FL client selection focus on the perspective of improving model training efficiency and performance, without holistically considering potential misbehavior and the cost of hiring. To bridge this gap, we propose a first-of-its-kind reputation-aware S tochastic integer programming-based FL C lient S election method (SCS). It can optimally select and compensate clients with different reputation profiles. Extensive experiments show that SCS achieves the most advantageous performance-cost trade-off compared to other existing state-of-the-art approaches.
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ISSN:2332-7790
2372-2096
DOI:10.1109/TBDATA.2022.3191332