Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

Paper recorded by Signals 4 on 2026-09-14 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-14 on arXiv · recorded by Signals 4 on 2026-09-15

Category: cs.AI · 人工智能 · first seen 2026-09-15

Abstract

This study proposes a privacy-enhanced federated learning framework to address secure collaborative training in distributed data environments. The framework integrates Dynamic Differential Privacy (DDP), lightweight Homomorphic Encryption (HE), and Local Differential Privacy (LDP) mechanisms to ensure data privacy protection during model training. Additionally, the framework employs an asynchronou

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