Wrapping Optuna and Ray Tune into a PySyft federated learning pipeline with per-round client feedback yields higher test accuracy than random search on FEMNIST and CIFAR10, while the proposed step-wise feedback mechanism itself is not ablated.
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Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism
Wrapping Optuna and Ray Tune into a PySyft federated learning pipeline with per-round client feedback yields higher test accuracy than random search on FEMNIST and CIFAR10, while the proposed step-wise feedback mechanism itself is not ablated.