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This content will become publicly available on July 23, 2024

Title: On the Convergence of Federated Averaging with Cyclic Client Participation
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL). Previous convergence analyses of FedAvg either assume full client participation or partial client participation where the clients can be uniformly sampled. However, in practical cross-device FL systems, only a subset of clients that satisfy local criteria such as battery status, network connectivity, and maximum participation frequency requirements (to ensure privacy) are available for training at a given time. As a result, client availability follows a natural cyclic pattern. We provide (to our knowledge) the first theoretical framework to analyze the convergence of FedAvg with cyclic client participation with several different client optimizers such as GD, SGD, and shuffled SGD. Our analysis discovers that cyclic client participation can achieve a faster asymptotic convergence rate than vanilla FedAvg with uniform client participation under suitable conditions, providing valuable insights into the design of client sampling protocols.  more » « less
Award ID(s):
2045694
NSF-PAR ID:
10489307
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Proceedings on Machine Learning Research
Date Published:
Journal Name:
International Conference on Machine Learning (ICML)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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