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FedBayes A Zero Trust Federated Learning Aggregation To Defend Against Adversarial Attacks by lakovo2104 is a document available to read on EtoBox.

What is FedBayes A Zero Trust Federated Learning Aggregation To Defend Against Adversarial Attacks about?

The document presents FedBayes, a novel aggregation method for federated learning designed to defend against adversarial attacks by calculating the probabilities of client model weights based on prior global model weights. This approach aims to mitigate the influence of malicious clients while still allowing benign clients to contribute to the global model. The paper discusses the methodology, experiments, and results, demonstrating that FedBayes effectively protects the federation from corrupted updates.

Author

lakovo2104

Language

EN