Federated learning can protect the data privacy of edge devices in collaborative training of edge devices. In the general FL scenarios, the participants of FL are usually composed of heterogeneous edge devices, where resource-constrained devices will consume more time, resulting in the decline of the training speed. The existing schemes either ignore stragglers, or distribute the computing tasks according to the distributed idea, but the distribution process involves the transmission of raw data, which cannot guarantee data privacy. To alleviate the straggler problem in small or medium-sized multiple heterogeneous devices scenario, this paper proposed a coding-based FL scheme, and it designed an efficient scheduling algorithm combined with the mathematical characteristics of linear coding to ensure data privacy and accelerate the speed of heterogeneous FL system. Meanwhile, the experimental results completed in the actual experimental platform show that when the performance difference between heterogeneous devices is large, the coding-based FL scheme can shorten the training time of the straggler by 92.85%.

[2022-11-14] Accepted Paper /doc/2022.08.0449/v1

[2023-04-05] Printed Article /doc/2022.08.0449/v2