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Prune at the Clients, Not the Server: Accelerated Sparse Training in Federated Learning
On the other hand, communication costs in FL can be addressed by local training, where each client takes multiple gradient steps on its local data.
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
Federated Learning (FL) has garnered increasing attention due to its unique characteristic of allowing heterogeneous clients to process their private data locally and interact with a central server, while being respectful of privacy.
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