利用闲置边缘设备算力，实现去中心化可持续大模型训练
Towards Decentralized and Sustainable Foundation Model Training with the Edge
- 用联网边缘设备的闲置算力替代集中式数据中心
- 可降低大模型训练的碳足迹，提升能源效率
- 适合关注绿色AI与分布式计算的研究者
基础模型是当前人工智能研究的核心，能够从海量数据中学习并适应多种任务。然而，其巨大的计算需求带来了环境影响和开发权过度集中的风险。本文提出一种去中心化且可持续的基础模型训练愿景，充分利用低利用率的联网边缘AI设备的集体算力。我们阐述了该愿景的合理性，特别是其在可持续性方面的优势，并进一步梳理了实现该愿景所需克服的一系列挑战。
原文摘要 · Abstract (English)
Foundation models are at the forefront of AI research, appealing for their ability to learn from vast datasets and cater to diverse tasks. Yet, their significant computational demands raise issues of environmental impact and the risk of centralized control in their development. We put forward a vision towards decentralized and sustainable foundation model training that leverages the collective compute of sparingly used connected edge AI devices. We present the rationale behind our vision, particularly in support of its sustainability benefit. We further outline a set of challenges that need to be addressed to turn this vision into reality.
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