The author proposes a novel approach integrating consensus-based verification and adaptive thresholding to fortify Federated Learning against label-flipping attacks, ensuring model integrity and reliability in distributed environments.
Consensus-based label verification and adaptive thresholding enhance security in federated learning against label-flipping attacks.