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Found 3 papers, 1 papers with code
FedAUXfdp: Differentially Private One-Shot Federated Distillation
Federated learning suffers in the case of non-iid local datasets, i. e., when the distributions of the clients' data are heterogeneous.
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning
Federated Distillation (FD) is a popular novel algorithmic paradigm for Federated Learning, which achieves training performance competitive to prior parameter averaging based methods, while additionally allowing the clients to train different model architectures, by distilling the client predictions on an unlabeled auxiliary set of data into a student model.
Communication-Efficient Federated Distillation
Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems.
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