
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.


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enhancing-security-in-federated-learning-through-adaptive-consensus-based-model-update-validation


Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation


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Consensus-based label verification and adaptive thresholding enhance security in federated learning against label-flipping attacks.


federated-learning-security-enhancement-with-consensus-based-model-validation


Federated Learning Security Enhancement with Consensus-Based Model Validation
