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Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data
Federated learning enables multiple institutions to collaboratively train a shared model without exchanging their raw data. However, individual model updates are data-dependent and may reveal information about clients' local training data. This paper presents a privacy-preserving federated learning
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