FedMHO is a hybrid one-shot federated learning framework where resource-sufficient clients contribute deep classifiers and resource-constrained clients contribute lightweight generative models, fused on the server into a single global model.
Advances and open problems in federated learning,
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FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices
FedMHO is a hybrid one-shot federated learning framework where resource-sufficient clients contribute deep classifiers and resource-constrained clients contribute lightweight generative models, fused on the server into a single global model.