Heterogeneous coding-based federated learning

doi:10.3969/j.issn.1000-5641.2023.05.010

Journal of East China Normal University(Natural Science) ›› 2023, Vol. 2023 ›› Issue (5): 110-121.doi: 10.3969/j.issn.1000-5641.2023.05.010

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Heterogeneous coding-based federated learning

Hongwei SHI¹(), Daocheng HONG^2,*(), Lianmin SHI^3,⁴, Yingyao YANG³

1. School of Information Engineering, Suqian University, Suqian, Jiangsu　223800, China
2. Shanghai Institute of AI for Education & School of Computer Science and Technology, East China Normal University, Shanghai　200062, China
3. School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu　215008, China
4. The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions, Wuyi University, Wuyishan, Fujian　354300, China

Received:2023-07-19 Accepted:2023-07-19 Online:2023-09-25 Published:2023-09-20
Contact: Daocheng HONG E-mail:shwtongxin@squ.edu.cn;hongdc@dase.ecnu.edu.cn

Abstract

Abstract:

In heterogeneous federated learning systems, among a variety of edge devices such as personal computers and embedded devices, resource-constrained devices, i.e. stragglers, reduce the training efficiency of the federated learning system. This paper proposes a heterogeneous coded federated learning (HCFL) system to ① improve the training efficiency of the system and speed up the training of heterogeneous federated learning (FL) for multiple stragglers, ② provide a certain level of data privacy protection. The HCFL scheme designs scheduling strategies from the perspective of client and server to satisfy the accelerated calculation of multiple stragglers model in the general environment. In addition, a linear coded computing (LCC) scheme is designed to provide data protection for task distribution. The experimental results show that HCFL can reduce training time by 89.85% when the performance difference between devices is large.

Key words: federated learning, linear coding, heterogeneous system, scheduling algorithm

CLC Number:

TP181

Hongwei SHI, Daocheng HONG, Lianmin SHI, Yingyao YANG. Heterogeneous coding-based federated learning[J]. Journal of East China Normal University(Natural Science), 2023, 2023(5): 110-121.

Figures/Tables 6

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References 21

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