Interactive Byzantine-Resilient Gradient Coding for General Data Assignments

Shreyas Jain, Luis Maßny, Christoph Hofmeister, Eitan Yaakobi, Rawad Bitar

Abstract: We tackle the problem of Byzantine errors in dis-tributed 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.