research-article

EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control

Authors:

Zhiwen YuAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 8, Issue 1

Article No.: 41, Pages 1 - 22

https://doi.org/10.1145/3643560

Published: 06 March 2024 Publication History

Abstract

The rise of mobile devices with abundant sensory data and local computing capabilities has driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) emerges to train specific deep models for each mobile device to address data heterogeneity and varying performance preferences. However, mobile training times vary significantly, resulting in either delay (when waiting for slower devices for aggregation) or accuracy decline (when aggregation proceeds without waiting). In response, we propose a shift towards asynchronous PFL, where the server aggregates updates as soon as they are available. Nevertheless, existing asynchronous protocols are unfit for PFL because they are devised for federated training of a single global model. They suffer from slow convergence and decreased accuracy when confronted with severe data heterogeneity prevalent in PFL. Furthermore, they often exclude slower devices for staleness control, which notably compromises accuracy when these devices possess critical personalized data. Therefore, we propose EchoPFL, a coordination mechanism for asynchronous PFL. Central to EchoPFL is to include updates from all mobile devices regardless of their latency. To cope with the inevitable staleness from slow devices, EchoPFL revisits model broadcasting. It intelligently converts the unscalable broadcast to on-demand broadcast, leveraging the asymmetrical bandwidth in wireless networks and the dynamic clustering-based PFL. Experiments show that compared to status quo approaches, EchoPFL achieves a reduction of up to 88.2% in convergence time, an improvement of up to 46% in accuracy, and a decrease of 37% in communication costs.

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Cited By

Li XLiu SZhou ZXu YGuo BYu Z(2025)ClassTer: Mobile Shift-Robust Personalized Federated Learning via Class-Wise ClusteringIEEE Transactions on Mobile Computing10.1109/TMC.2024.348729424:3(2014-2028)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3487294
GAO YLIU SGUO BXU XBIAN HHAO JXU WYU Z(2024)Lightweight sensing-computing-decision collaboration enhancement for multi-mobile terminalsSCIENTIA SINICA Informationis10.1360/SSI-2024-008954:9(2136)Online publication date: 9-Sep-2024
https://doi.org/10.1360/SSI-2024-0089
Esquivel JAldous BAbdelmoniem AAlhilal AYou L(2024)Performance Profiling of Federated Learning Across Heterogeneous Mobile Devices2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00053(363-372)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS-C63300.2024.00053

Index Terms

EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 8, Issue 1

March 2024

1182 pages

EISSN:2474-9567

DOI:10.1145/3651875

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Published: 06 March 2024

Published in IMWUT Volume 8, Issue 1

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Cited By

Li XLiu SZhou ZXu YGuo BYu Z(2025)ClassTer: Mobile Shift-Robust Personalized Federated Learning via Class-Wise ClusteringIEEE Transactions on Mobile Computing10.1109/TMC.2024.348729424:3(2014-2028)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3487294
GAO YLIU SGUO BXU XBIAN HHAO JXU WYU Z(2024)Lightweight sensing-computing-decision collaboration enhancement for multi-mobile terminalsSCIENTIA SINICA Informationis10.1360/SSI-2024-008954:9(2136)Online publication date: 9-Sep-2024
https://doi.org/10.1360/SSI-2024-0089
Esquivel JAldous BAbdelmoniem AAlhilal AYou L(2024)Performance Profiling of Federated Learning Across Heterogeneous Mobile Devices2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00053(363-372)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS-C63300.2024.00053

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