2022
Tempo: Improving Training Performance in Cross-Silo Federated Learning
ICASSP 2022accepted
Different from its commonly studied scenario to centrally store clients’ data in institutions, which implicitly neglects clients’ data privacy, we study cross-silo federated learning in a preferable setting to keep private data on clients, and train the global model with a three-layer structure, whe…