  Recent advances in distributed artificial intelligence (AI) have led to
tremendous breakthroughs in various communication services, from fault-tolerant
factory automation to smart cities. When distributed learning is run over a set
of wirelessly connected devices, random channel fluctuations and the incumbent
services running on the same network impact the performance of both distributed
learning and the coexisting service. In this paper, we investigate a mixed
service scenario where distributed AI workflow and ultra-reliable low latency
communication (URLLC) services run concurrently over a network. Consequently,
we propose a risk sensitivity-based formulation for device selection to
minimize the AI training delays during its convergence period while ensuring
that the operational requirements of the URLLC service are met. To address this
challenging coexistence problem, we transform it into a deep reinforcement
learning problem and address it via a framework based on soft actor-critic
algorithm. We evaluate our solution with a realistic and 3GPP-compliant
simulator for factory automation use cases. Our simulation results confirm that
our solution can significantly decrease the training delay of the distributed
AI service while keeping the URLLC availability above its required threshold
and close to the scenario where URLLC solely consumes all network resources.
