Byzantine-Robust and Communication-Efficient Distributed Training: Compressive and Cyclic Gradient Coding
In this paper, we study the problem of distributed training (DT) under Byzantine attacks with communication constraints. While prior work has developed various robust aggregation rules at the server to enhance robustness to Byzantine attacks, the existing methods suffer from a critical limitation in that the solution error does not diminish when the local gradients sent by different devices vary considerably, as a result of data heterogeneity among the subsets held by different devices. To overcome this limitation, we propose a novel DT method, cyclic gradient coding-based DT (LAD). In LAD, the server allocates the entire training dataset to the devices before training begins. In each ite...
| Authors | Chengxi Li, Youssef Allouah, Rachid Guerraoui et al. |
|---|---|
| Primary category | cs.DC |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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