区分分布式与去中心化训练，揭示AI治理新挑战
Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape
- 区分分布式与去中心化训练，避免政策混淆
- 去中心化加剧算力结构风险与能力扩散，降低可检测性
- 适合关注算力治理、去中心化AI政策的决策者
低通信量训练算法的进步正推动模型训练从集中式向跨集群分布式或社区驱动的去中心化模式转变。本文明确区分这两种场景，它们在政策讨论中常被混淆。我们分析其对技术人工智能治理的影响：可能加剧算力结构化、能力扩散，并削弱可检测性和关停能力。尽管这些趋势预示着可能挑战现有算力治理假设的新范式，但某些政策工具（如出口管制）仍具相关性。同时，去中心化AI也带来隐私保护训练、数据获取拓展及权力集中缓解等潜在益处。本文旨在支持更精准的算力、能力扩散与去中心化AI发展政策制定。
原文摘要 · Abstract (English)
Advances in low-communication training algorithms are enabling a shift from centralised model training to compute setups that are either distributed across multiple clusters or decentralised via community-driven contributions. This paper distinguishes these two scenarios - distributed and decentralised training - which are little understood and often conflated in policy discourse. We discuss how they could impact technical AI governance through an increased risk of compute structuring, capability proliferation, and the erosion of detectability and shutdownability. While these trends foreshadow a possible new paradigm that could challenge key assumptions of compute governance, we emphasise that certain policy levers, like export controls, remain relevant. We also acknowledge potential benefits of decentralised AI, including privacy-preserving training runs that could unlock access to more data, and mitigating harmful power concentration. Our goal is to support more precise policymaking around compute, capability proliferation, and decentralised AI development.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。