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arXiv · 2401.16915

Interactive Byzantine-Resilient Gradient Coding for General Data Assignments

Shreyas Jain·Luis Maßny·Christoph Hofmeister·Eitan Yaakobi·Rawad Bitar
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Abstract

We tackle the problem of Byzantine errors in distributed gradient descent within the Byzantine-resilient gradient coding framework. Our proposed solution can recover the exact full gradient in the presence of $s$ malicious workers with a data replication factor of only $s+1$. It generalizes previous solutions to any data assignment scheme that has a regular replication over all data samples. The scheme detects malicious workers through additional interactive communication and a small number of local computations at the main node, leveraging group-wise comparisons between workers with a provably optimal grouping strategy. The scheme requires at most $s$ interactive rounds that incur a total communication cost logarithmic in the number of data samples.

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Shreyas Jain, Luis Maßny, Christoph Hofmeister, Eitan Yaakobi, Rawad Bitar. 2024-01-30. Interactive Byzantine-Resilient Gradient Coding for General Data Assignments. https://arxiv.org/abs/2401.16915

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