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

• System for Learning from Data • Previous Articles    

Heterogeneous coding-based federated learning

Hongwei SHI1(), Daocheng HONG2,*(), Lianmin SHI3,4, Yingyao YANG3   

  1. 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:2023-09-15
  • Contact: Daocheng HONG;


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: