Abstract: The federated learning (FL) client selection scheme can effectively mitigate global model performance degradation caused by the random aggregation of clients with heterogeneous data.
FedPPO: Reinforcement Learning-Based Client Selection for Federated Learning With Heterogeneous Data
Abstract: Federated Learning (FL) enables multiple data owners to jointly train a machine learning model, which can improve joint environmental cognitive capability without disclosing their private ...
UnitRefine is an open-source machine-learning framework for automated spike-sorting curation in electrophysiological experiments. UnitRefine is agnostic to probe type, species, brain region, or spike ...
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