Byzantine-Robust Variance-Reduced Federated Learning over Distributed Non-i.i.d. Data
This addresses robust distributed learning for applications with heterogeneous data and security threats, representing an incremental improvement over existing methods.
The paper tackles federated learning with non-i.i.d. data and Byzantine attacks by using a resampling strategy and stochastic average gradient to reduce variance, achieving linear convergence with error dependent on the number of malicious workers, as shown in numerical experiments outperforming state-of-the-art methods.
We consider the federated learning problem where data on workers are not independent and identically distributed (i.i.d.). During the learning process, an unknown number of Byzantine workers may send malicious messages to the central node, leading to remarkable learning error. Most of the Byzantine-robust methods address this issue by using robust aggregation rules to aggregate the received messages, but rely on the assumption that all the regular workers have i.i.d. data, which is not the case in many federated learning applications. In light of the significance of reducing stochastic gradient noise for mitigating the effect of Byzantine attacks, we use a resampling strategy to reduce the impact of both inner variation (that describes the sample heterogeneity on every regular worker) and outer variation (that describes the sample heterogeneity among the regular workers), along with a stochastic average gradient algorithm to gradually eliminate the inner variation. The variance-reduced messages are then aggregated with a robust geometric median operator. We prove that the proposed method reaches a neighborhood of the optimal solution at a linear convergence rate and the learning error is determined by the number of Byzantine workers. Numerical experiments corroborate the theoretical results and show that the proposed method outperforms the state-of-the-arts in the non-i.i.d. setting.