  Edge devices with local computation capability has made distributed deep
learning training on edges possible. In such method, the cluster head of a
cluster of edges schedules DL training jobs from the edges. Using such
centralized scheduling method, the cluster head knows all loads of edges, which
can avoid overloading the cluster edges, but the head itself may become
overloaded. To handle this problem, we propose a multi-agent RL (MARL) system
that enables each edge to schedule its jobs using RL. However, without
coordination among edges, action collision may occur, in which multiple edges
schedule tasks to the same edge and make it overloaded. For this reason, we
propose a system called Shielded ReinfOrcement learning (RL) based DL training
on Edges (SROLE). In SROLE, the shield deployed in an edge checks action
collisions and provides alternative actions to avoid collisions. As the central
shield for entire cluster may become a bottleneck, we further propose a
decentralized shielding method, where different shields are responsible for
different regions in the cluster and they coordinate to avoid action collisions
on the region boundaries. Our emulation and real device experiments show SROLE
reduces training time by 59% compared to MARL and centralized RL.
