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cs.DC2024
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Addressing intermittent client availability is critical for the real-world deployment of federated learning algorithms. Most prior work either overlooks the potential non-stationar…
cs.DC2024
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Federated learning is a popular distributed learning approach for training a machine learning model without disclosing raw data. It consists of a parameter server and a possibly la…