Cooperative Gradient Coding for Collaborative Federated Learning
arxiv(2024)
摘要
We investigate federated learning (FL) in the presence of stragglers, with
emphasis on wireless scenarios where the power-constrained edge devices
collaboratively train a global model on their local datasets and transmit local
model updates through fading channels. To tackle stragglers resulting from link
disruptions without requiring accurate prior information on connectivity or
dataset sharing, we propose a gradient coding (GC) scheme based on cooperative
communication, which remains valid for general collaborative federated
learning. Furthermore, we conduct an outage analysis of the proposed scheme,
based on which we conduct the convergence analysis. The simulation results
reveal the superiority of the proposed strategy in the presence of stragglers,
especially under imbalanced data distribution.
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