RobustFed: A Truth Inference Approach for Robust Federated Learning

Abstract

Federated learning is a prominent framework that enables clients (e.g., mobile devices or organizations) to collaboratively train a global model under a central server's orchestration while keeping local data private. However, the aggregation step in federated learning is vulnerable to adversarial attacks as the central server cannot enforce clients' behavior. As a result, the performance of the global model and convergence of the training process can be affected under such attacks. To mitigate this vulnerability, existing works have proposed robust aggregation methods such as median based aggregation instead of averaging. While they ensure some robustness against Byzantine attacks, they are still vulnerable to label flipping and Gaussian noise attacks. In this paper, we propose a novel robust aggregation algorithm inspired by the truth inference methods in crowdsourcing by incorporating the clients' reliability into aggregation. We evaluate our solution on three real-world datasets with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning and is resilient to various types of attacks, including noisy data attacks, Byzantine attacks, and label flipping attacks.

Topics & Concepts