Interactive Byzantine-Resilient Gradient Coding for General Data Assignments
Shreyas Jain, Luis Maßny, Christoph Hofmeister, Eitan Yaakobi, Rawad Bitar · 2024
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.