{
  "id": 16577,
  "url": "https://arxiv.org/abs/2608.03324v1",
  "title": "AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning",
  "summary": "Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy. To facilitate practical deployment on resource-constrained edge devices, Spiking Neural Networks (SNNs) have emerged as a promising alternative to traditional Artificial Neural Networks (ANNs) due to their sparse computing mechanisms and high energy efficiency. However, jointly training ANNs and SNNs exposes a challenge of representational misalignment, which is intrins",
  "authors": "Shengyang Li, Yiting Dong, Liuyang Song, Ximing Wang, Luyuan Xie, Cong Li et al.",
  "category": "research",
  "topics": "safety-alignment,privacy-surveillance,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-04T08:32:30.000Z",
  "fetched_at": "2026-08-05T05:10:44.550Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16577",
  "original_url": "https://arxiv.org/abs/2608.03324v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}