  Big Artificial Intelligence (AI) models have emerged as a crucial element in
various intelligent applications at the edge, such as voice assistants in smart
homes and autonomous robotics in smart factories. Training big AI models, e.g.,
for personalized fine-tuning and continual model refinement, poses significant
challenges to edge devices due to the inherent conflict between limited
computing resources and intensive workload associated with training. Despite
the constraints of on-device training, traditional approaches usually resort to
aggregating training data and sending it to a remote cloud for centralized
training. Nevertheless, this approach is neither sustainable, which strains
long-range backhaul transmission and energy-consuming datacenters, nor safely
private, which shares users' raw data with remote infrastructures. To address
these challenges, we alternatively observe that prevalent edge environments
usually contain a diverse collection of trusted edge devices with untapped idle
resources, which can be leveraged for edge training acceleration. Motivated by
this, in this article, we propose collaborative edge training, a novel training
mechanism that orchestrates a group of trusted edge devices as a resource pool
for expedited, sustainable big AI model training at the edge. As an initial
step, we present a comprehensive framework for building collaborative edge
training systems and analyze in-depth its merits and sustainable scheduling
choices following its workflow. To further investigate the impact of its
parallelism design, we empirically study a case of four typical parallelisms
from the perspective of energy demand with realistic testbeds. Finally, we
discuss open challenges for sustainable collaborative edge training to point to
future directions of edge-centric big AI model training.
