A Secure and Private Distributed Bayesian Federated Learning Design

Ali NematiFeb 2421 sec read26 views

Researchers propose a new Distributed Federated Learning framework that addresses privacy leakage, slow convergence, and Byzantine attacks through Bayesian model training and optimal neighbor selection using reinforcement learning. This approach enhances robustness and efficiency in decentralized machine learning systems without compromising security or privacy.

Read the full article at arXiv cs.LG (ML)

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