Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning. (arXiv:2206.05359v2 [cs.CR] UPDATED)

Federated learning (FL) facilitates distributed training across clients,
safeguarding the privacy of their data. The inherent distributed structure of
FL introduces vulnerabilities, especially from adversarial (Byzantine) clients
aiming to skew local updates to their advantage. Despite the plethora of
research focusing on Byzantine-resilient FL, the academic community has yet to
establish a comprehensive benchmark suite, pivotal for impartial assessment and
comparison of different techniques.

This paper investigates existing techniques in Byzantine-resilient FL and
introduces an open-source benchmark suite for convenient …

adversarial attacks benchmark clients data distributed federated learning local privacy research training updates vulnerabilities