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Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation

arXiv:2509.25906v2 Announce Type: replace Abstract: Federated Learning (FL) often adopts differential privacy (DP) to protect client data, but the added noise required for privacy guarantees can substantially degrade model accuracy. To resolve this challenge, we propose model-splitting privacy-amplified federated learning (MS-PAFL), a novel framework that combines structural model splitting with statistical privacy amplification. In this framework, each client's model is partitioned into a...

arXiv CS 9d ago