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cs.LG2024
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning
Georg Meinhardt, Kai Yi, Laurent Condat +1
In Federated Learning (FL), both client resource constraints and communication costs pose major problems for training large models. In the centralized setting, sparse training addr…
cs.LG2024
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
Kai Yi, Georg Meinhardt, Laurent Condat +1
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…