HAL (France)
open access
CLIN-FL: Cost-efficient Load-balancing for In-Network Federated Learning with non-IID data
Federated Learning allows training machine learning models across many devices without sharing their raw data, which makes it attractive for deployment in telecommunication and in-network computing environments. However, real-world network data are often heterogeneous, and feature distributi
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