Better Together: Attaining the Triad of Byzantine-robust Federated Learning via Local Update Amplification
Liyue Shen, Yanjun Zhang, Jingwei Wang, Guangdong Bai · 2022
Manipulation of local training data and local updates, i.e., the Byzantine poisoning attack, is the main threat arising from the collaborative nature of the federated learning (FL) paradigm. Many Byzantine-robust aggregation algorithms (AGRs) have been proposed to filter out or moderate suspicious local updates uploaded by Byzantine participants at the central aggregator. However, they largely suffer from model quality degradation due to the over-removal of local updates or/and the inefficiency caused by the expensive analysis of the high-dimensional local updates.