Byzantine-Robust and Communication-Efficient Distributed Training: Compressive and Cyclic Gradient Coding
📰 ArXiv cs.AI
Distributed training under Byzantine attacks with communication constraints can be improved with compressive and cyclic gradient coding
Action Steps
- Develop compressive gradient coding to reduce communication overhead
- Implement cyclic gradient coding to enhance robustness to Byzantine attacks
- Combine compressive and cyclic gradient coding for improved performance
- Evaluate the proposed method in heterogeneous data environments
Who Needs to Know This
Machine learning engineers and researchers on a team can benefit from this paper as it provides a solution to enhance robustness to Byzantine attacks in distributed training, and data scientists can apply these methods to improve model training in heterogeneous data environments
Key Insight
💡 Compressive and cyclic gradient coding can enhance robustness to Byzantine attacks in distributed training with communication constraints
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💡 Improve distributed training robustness with compressive & cyclic gradient coding!
Key Takeaways
Distributed training under Byzantine attacks with communication constraints can be improved with compressive and cyclic gradient coding
Full Article
Title: Byzantine-Robust and Communication-Efficient Distributed Training: Compressive and Cyclic Gradient Coding
Abstract:
arXiv:2603.28780v1 Announce Type: cross Abstract: 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 amo
Abstract:
arXiv:2603.28780v1 Announce Type: cross Abstract: 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 amo
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