AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2024

AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices

Ji Liu
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  • PersonId : 1390556
Tianshi Che
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Yang Zhou
Ruoming Jin
Huaiyu Dai

Abstract

Federated Learning (FL) has achieved significant achievements re- cently, enabling collaborative model training on distributed data over edge devices. Iterative gradient or model exchanges between devices and the centralized server in the standard FL paradigm suf- fer from severe efficiency bottlenecks on the server. While enabling collaborative training without a central server, existing decentral- ized FL approaches either focus on the synchronous mechanism that deteriorates FL convergence or ignore device staleness with an asynchronous mechanism, resulting in inferior FL accuracy. In this paper, we propose an Asynchronous Efficient Decentralized FL framework, i.e., AEDFL, in heterogeneous environments with three unique contributions. First, we propose an asynchronous FL sys- tem model with an efficient model aggregation method for improv- ing the FL convergence. Second, we propose a dynamic staleness- aware model update approach to achieve superior accuracy. Third, we propose an adaptive sparse training method to reduce commu- nication and computation costs without significant accuracy degra- dation. Extensive experimentation on four public datasets and four models demonstrates the strength of AEDFL in terms of accuracy (up to 16.3% higher), efficiency (up to 92.9% faster), and computa- tion costs (up to 42.3% lower).
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Dates and versions

lirmm-04597263 , version 1 (02-06-2024)

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Ji Liu, Tianshi Che, Yang Zhou, Ruoming Jin, Huaiyu Dai, et al.. AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices. SDM 2024 - SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Apr 2024, Houston, TX, United States. pp.833-841, ⟨10.1137/1.9781611978032.95⟩. ⟨lirmm-04597263⟩
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