dynamic differential privacy
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Adaptive Trust-Aware Post-Quantum Secure Federated Learning with Dynamic Differential Privacy
Abstract: FL allows multiple users to train the machine learning model and ensure that they cannot share their raw data. Post-quantum secure aggregation and differential privacy for FL are introduced by the Beskar Framework; it assumes that all selected nodes will perform reliably. Due to hardware faults, network failures, or malicious intent, some of the assisting nodes may perform unreliably while we are developing it in the real-world. To assist the …
Published in Journal of Advanced Database Management & Systems · Vol. 13, Issue 2, 2026 · pp. 38–44 Read article