Comprehensive Evaluation of Federated Multi-Task Learning on Non-Independent and Identically Distributed Data Silos

This paper introduces a comprehensive benchmark, FMTL-Bench, to systematically evaluate the Federated Multi-Task Learning (FMTL) paradigm by considering data, model, and optimization algorithm levels. The benchmark covers various non-IID data partitioning scenarios and provides valuable insights into the strengths and limitations of existing baseline methods for optimal FMTL application.