Cited By
View all- Chen CLiu JTan HLi XWang KLi PSakurai KDou D(2024)Trustworthy federated learning: privacy, security, and beyondKnowledge and Information Systems10.1007/s10115-024-02285-2Online publication date: 26-Nov-2024
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
Authors: 
Ji Liu, Juncheng Jia, Hong Zhang, Yuhui Yun, 
Leye Wang, Yang Zhou, Huaiyu Dai, Dejing DouAuthors Info & Claims
Ji Liu, Juncheng Jia, Hong Zhang, Yuhui Yun, Leye Wang, Yang Zhou, Huaiyu Dai, Dejing DouAuthors Info & ClaimsArticle No.: 122, Pages 1 - 28
Abstract
Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller).
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Publication History
Published: 20 November 2024
Online AM: 02 September 2024
Accepted: 31 July 2024
Revised: 02 January 2024
Received: 29 December 2022
Published in TIST Volume 15, Issue 6
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View all- Chen CLiu JTan HLi XWang KLi PSakurai KDou D(2024)Trustworthy federated learning: privacy, security, and beyondKnowledge and Information Systems10.1007/s10115-024-02285-2Online publication date: 26-Nov-2024
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