SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

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 asynchronous aggregation strategy with version control to support distributed training in asynchronous environments. Experimental validation on the CIFAR-10 and Purchase-100 benchmark datasets demonstrates that

Read original source ↗ Open in workspace

recordType

paper

region

Global