A triple-shuffle serial federated learning method with Shapley-based contribution scoring is claimed to outperform parallel and serial baselines on non-IID healthcare data.
Distributed deep learning networks among institutions for medical imaging,
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TriCon-SF: A Triple-Shuffle and Contribution-Aware Serial Federated Learning Framework for Heterogeneous Healthcare Data
A triple-shuffle serial federated learning method with Shapley-based contribution scoring is claimed to outperform parallel and serial baselines on non-IID healthcare data.