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Efficient Secure Aggregation for Federated Learning

Efficient Secure Aggregation for Federated Learning

Federated Learning (FL) trains a global model by having each selected device push only its model update to a central server, keeping raw data local. However, those updates can still leak sensitive information unless the server learns only their sum. A naïve approach is to run a generic secure‑multiparty sum, but off‑the‑shelf protocols ...

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