{
  "id": 17933,
  "url": "https://arxiv.org/abs/2608.06946v1",
  "title": "Stream Learning: Partition-Fair Gossip Learning Without Tokens",
  "summary": "In gossip learning, a network of nodes trains a shared model collaboratively, without a central coordinator, by repeatedly exchanging parts of their local models. The state-of-the-art protocol, Partitioned Token Gossip Learning (PTGL) of Heged{ü}s et al., splits the weight matrix into S fixed partitions and disseminates them using a token-based fairness mechanism coupled with per-neighbor metadata exchange. We revisit partition scheduling by analogy with peer-to-peer live streaming, where model",
  "authors": "Fabien Mathieu, Alexandre Pham, Maria Gradinariu Potop-Butucaru, S{é}bastien Tixeuil",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T08:22:37.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17933",
  "original_url": "https://arxiv.org/abs/2608.06946v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}