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FedLPA: Personalized One-shot Federated Learning with Layer-Wise Posterior Aggregation
Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning.
A Bayesian Federated Learning Framework with Online Laplace Approximation
On the client side, a prior loss that uses the global posterior probabilistic parameters delivered from the server is designed to guide the local training.
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