Backdoor Attacks on Federated Learning

Federated Learning has emerged as a powerful paradigm for decentralized machine learning, enabling col laborative model training without sharing raw data. While this preserves privacy, it also introduces vulnerabilities: malicious participants can submit corrupted updates, leading to poisoning, backdoors, or convergence failures that propagate through the global model.

Unpublished