Federated Learning Robust to Byzantine Attacks: Achieving Zero
  Optimality Gap
Shiyuan Zuo Rongfei Fan Han Hu Ning Zhang Shimin Gong
In this paper, we propose a robust aggregation method for federated learning
(FL) that can effectively tackle malicious Byzantine attacks. At each user,
model parameter is firstly updated by multiple steps, which is adjustable over
iterations, and then pushed to the aggregation center directly. This decreases
the number of interactions between the aggregation center and users, allows
each user to set training parameter in a flexible way, and reduces computation
burden compared with existing works that need to combine multiple historical
model parameters. At the aggregation center, geometric median is leveraged to
combine the received model parameters from each user. Rigorous proof shows that
zero optimality gap is achieved by our proposed method with linear convergence,
as long as the fraction of Byzantine attackers is below half. Numerical results
verify the effectiveness of our proposed method.