{
  "id": 18410,
  "url": "https://arxiv.org/abs/2608.10929v1",
  "title": "FedCGR: Federated Cross-Domain Generative Recommendation",
  "summary": "Cross-domain recommendation (CDR) transfers preference knowledge across related domains, but federated deployment makes cross-domain alignment difficult because the behavioral anchors that align item spaces, such as overlapping users and shared interaction signals, are often sparse, unavailable, or privacy-sensitive across clients. To address this tension, we revisit federated CDR as generation over a stable semantic item language. By representing items as discrete semantic ID (SID) sequences de",
  "authors": "Zhuodong Liu, Hugen Lv, Xiangyu Li, Bohan Guo, Peiyu Hu",
  "category": "research",
  "topics": "safety-alignment,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-11T13:58:55.000Z",
  "fetched_at": "2026-08-12T05:10:43.828Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18410",
  "original_url": "https://arxiv.org/abs/2608.10929v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}