2024/01/30 by Shreyas Jain, Luis Maßny, Jain, Shreyas +7 · 1 citation
Computer Science · #Advanced Data Compression Techniques #Cellular Automata and Applications #Digital Image Processing Techniques #Distributed #FOS: Computer and information sciences #Information Theory (cs.IT) #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2401.16915
openalex publication_date 2024/01/30 · openalex created_date 2024/02/01 · openalex updated_date 2026/07/28
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.