60 citations · 107 across the 5 of their papers we have counts for
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cs.LG2023
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
Chenrui Wu, Zexi Li, Fangxin Wang +1
Federated learning (FL) is a distributed framework for collaboratively training with privacy guarantees. In real-world scenarios, clients may have Non-IID data (local class imbalan…
cs.LG2022★ 34 cited
Federated Unlearning with Knowledge Distillation
Chen Wu, Sencun Zhu, Prasenjit Mitra
Federated Learning (FL) is designed to protect the data privacy of each client during the training process by transmitting only models instead of the original data. However, the tr…