Paper Info
Title
An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee
Abstract
The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new paradigm to cope with the privacy issue by allowing clients to perform model training locally, without the necessity to upload their personal sensitive data. In FL, the number of clients could be sufficiently large, but the bandwidth available for model distribution and re-upload is quite limited, making it sensible to only involve part of the volunteers to participate in the training process. The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness. In this article, we will model the fairness guaranteed client selection as a Lyapunov optimization problem and then a C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> MAB-based method is proposed for estimation of the model exchange time between each client and the server, based on which we design a fairness guaranteed algorithm termed RBCS-F for problem-solving. The regret of RBCS-F is strictly bounded by a finite constant, justifying its theoretical feasibility. Barring the theoretical results, more empirical data can be derived from our real training experiments on public datasets.
Year
DOI
Venue
2021
10.1109/TPDS.2020.3040887
IEEE Transactions on Parallel and Distributed Systems
Keywords
DocType
Volume
Client selection,contextual combinatorial multi-arm bandit,fairness scheduling,federated learning,lyapunov optimization
Journal
32
Issue
ISSN
Citations 
7
1045-9219
6
PageRank 
References 
Authors
0.50
0
6
Name
Order
Citations
PageRank
Tiansheng Huang160.84
Weiwei Lin214713.95
Wentai Wu3343.77
Ligang He454256.73
Keqin Li52778242.13
albert y zomaya642743.75