AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices

Ji Liu; Tianshi Che; Yang Zhou; Ruoming Jin; Huaiyu Dai; Dejing Dou; Patrick Valduriez

doi:10.1137/1.9781611978032.95

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AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices

Ji Liu Tianshi Che Yang Zhou Ruoming Jin Huaiyu Dai Dejing Dou Patrick Valduriez

Year: 2024 Society for Industrial and Applied Mathematics eBooks Pages: 833-841 Publisher: Society for Industrial and Applied Mathematics

DOI: 10.1137/1.9781611978032.95

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Abstract

International audience

Keywords:

Asynchronous communication Computer science Asynchronous learning Federated learning Distributed computing Computer network Psychology Synchronous learning Mathematics education Cooperative learning

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Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Memory and Neural Computing

Physical Sciences → Engineering → Electrical and Electronic Engineering

Stochastic Gradient Optimization Techniques

Physical Sciences → Computer Science → Artificial Intelligence

AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices

Abstract

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Citation History

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