{
  "id": 16130,
  "url": "https://arxiv.org/abs/2608.01426v1",
  "title": "Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization",
  "summary": "Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability. In practical wireless networks, mobile devices (MDs) often exhibit diverse data and learning objectives, naturally forming clusters of users with jointly trainable models. When devices rely on energy harvesting (EH), stoc",
  "authors": "Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman",
  "category": "research",
  "topics": "privacy-surveillance,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-02T18:15:49.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/16130",
  "original_url": "https://arxiv.org/abs/2608.01426v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}