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Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design. (arXiv:2211.04132v1 [cs.DC])
Nov. 9, 2022, 2:20 a.m. | Yuchang Sun, Jiawei Shao, Yuyi Mao, Songze Li, Jun Zhang
cs.CR updates on arXiv.org arxiv.org
Federated learning (FL) has achieved great success as a privacy-preserving
distributed training paradigm, where many edge devices collaboratively train a
machine learning model by sharing the model updates instead of the raw data
with a server. However, the heterogeneous computational and communication
resources of edge devices give rise to stragglers that significantly decelerate
the training process. To mitigate this issue, we propose a novel FL framework
named stochastic coded federated learning (SCFL) that leverages coded computing
techniques. In SCFL, before …
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