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Federated Agent Optimization — arXiv:2610.01195

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.01195
License (as archived): CC0-1.0
Archived: 2026-10-06T12:04:05.765Z
SHA-256 of the archived text: aaf3076156c7cdb098d7603da95ae06a62a6b9a7794d637ef5969d09020ba8dc
Stable URL of this page: https://metraipolis.com/documento/dc_ed6fceea62bb

How to cite

Federated Agent Optimization — arXiv:2610.01195 — arXiv (en), https://arxiv.org/abs/2610.01195 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ed6fceea62bb (Metraipolis archive dc_ed6fceea62bb, sha256 aaf3076156c7cdb098d7603da95ae06a62a6b9a7794d637ef5969d09020ba8dc)

BibTeX

@misc{dced6fceea62bb,
  title = {Federated Agent Optimization — arXiv:2610.01195},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.01195},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ed6fceea62bb; sha256 aaf3076156c7cdb098d7603da95ae06a62a6b9a7794d637ef5969d09020ba8dc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.01195. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language model (LLM) agents increasingly operate in private environments and accumulate valuable experience from task execution, tool use, feedback, and local knowledge. Yet such experience is distributed across organizations and cannot be directly shared because of privacy and proprietary constraints. Conventional federated learning is insufficient for this setting, as agent capabilities extend beyond model parameters to memory, tools, rewards, skills, and structured knowledge. In this paper, we formulate \textbf{Federated Agent Optimization (FAO)}, which studies how distributed agents can collaboratively improve through controlled information exchange while keeping raw data, complete trajectories, and private knowledge local. We define FAO as a multi-objective problem balancing agent utility, privacy leakage, and communication cost, and organize its optimization space across policy, memory, tool use, reward, and structured knowledge and skills. We further characterize how private experience can be abstracted, protected, aggregated, and adapted into transferable capabilities, providing a unified view of how agents can benefit from one another without direct experience sharing. Finally, we identify the key challenges of FAO and outline several promising directions for future research toward trustworthy federated agent systems.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-02 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.01195 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T10:21:27.921Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).