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Found 3 papers, 2 papers with code
On the Generalization of Wasserstein Robust Federated Learning
We show that the robustness of WAFL is more general than related approaches, and the generalization bound is robust to all adversarial distributions inside the Wasserstein ball (ambiguity set).
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
In this work, we propose DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning.
Personalized Federated Learning with Moreau Envelopes
Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data.
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