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Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data
Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data exchange.
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
It includes a noise-resilient local solver and a robust global aggregator.
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