{
  "id": 7486884,
  "title": "Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation",
  "url": "https://urgent.news/2026/09/14/privacy-enhanced-federated-learning-via-asynchronous-aggregation-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-14T17:12:15.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.15885v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}