{
  "id": 6892,
  "url": "https://arxiv.org/abs/2603.21276v1",
  "title": "Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity",
  "summary": "Large language models (LLMs) increasingly adopt Mixture-of-Experts (MoE) architectures to scale model capacity while reducing computation. Fine-tuning these MoE-based LLMs often requires access to distributed and privacy-sensitive data, making centralized fine-tuning impractical. Federated learning (FL) therefore provides a paradigm to collaboratively fine-tune MoE-based LLMs, enabling each client to integrate diverse knowledge without compromising data privacy. However, the integration of MoE-b",
  "authors": "Zihan Fang, Qianru Wang, Haonan An, Zheng Lin, Yiqin Deng, Xianhao Chen et al.",
  "category": "research",
  "topics": "safety-alignment,privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-22T15:07:39.000Z",
  "fetched_at": "2026-07-14T16:32:50.145Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6892",
  "original_url": "https://arxiv.org/abs/2603.21276v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}