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Federated learning is a prominent framework that enables clients (e.g.,mobile devices or organizations) to train a collaboratively global model undera central server's orchestration while keeping local training datasets'privacy. However, the aggregation step in federated learning is vulnerable toadversarial attacks as the central server cannot manage clients' behavior.Therefore, the global model's performance and convergence of the trainingprocess will be affected under such attacks.To mitigate this vulnerabilityissue, we propose a novel robust aggregation algorithm inspired by the truthinference methods in crowdsourcing via incorporating the worker's reliabilityinto aggregation. We evaluate our solution on three real-world datasets with avariety of machine learning models. Experimental results show that our solutionensures robust federated learning and is resilient to various types of attacks,including noisy data attacks, Byzantine attacks, and label flipping attacks.

RobustFed: A Truth Inference Approach for Robust Federated Learning