Towards Decentralized and Sustainable Foundation Model Training with the Edge

TL;DR

This paper proposes a vision for training foundation models in a decentralized, sustainable manner by utilizing connected edge AI devices, aiming to reduce environmental impact and avoid centralized control.

Contribution

It introduces a novel approach to foundation model training that leverages edge devices, highlighting its sustainability benefits and outlining key challenges to implementation.

Findings

Highlights environmental benefits of edge-based training

Identifies technical challenges for decentralized training

Proposes a future research direction for sustainable AI

Abstract

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.