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FedDig: Robust Federated Learning Using Data Digest to Represent Absent Clients. (arXiv:2210.00737v2 [cs.LG] UPDATED)
Oct. 6, 2022, 1:13 a.m. | Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
cs.LG updates on arXiv.org arxiv.org
Federated Learning (FL) effectively protects client data privacy. However,
client absence or leaving during training can seriously degrade model
performances, particularly for unbalanced and non-IID client data. We address
this issue by generating data digests from the raw data and using them to guide
training at the FL moderator. The proposed FL framework, called FedDig, can
tolerate unexpected client absence in cross-silo scenarios while preserving
client data privacy because the digests de-identify the raw data by mixing
encoded features in …
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