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Recent advances in distributed artificial intelligence (AI) have led totremendous breakthroughs in various communication services, from fault-tolerantfactory automation to smart cities. When distributed learning is run over a setof wirelessly connected devices, random channel fluctuations and the incumbentservices running on the same network impact the performance of both distributedlearning and the coexisting service. In this paper, we investigate a mixedservice scenario where distributed AI workflow and ultra-reliable low latencycommunication (URLLC) services run concurrently over a network. Consequently,we propose a risk sensitivity-based formulation for device selection tominimize the AI training delays during its convergence period while ensuringthat the operational requirements of the URLLC service are met. To address thischallenging coexistence problem, we transform it into a deep reinforcementlearning problem and address it via a framework based on soft actor-criticalgorithm. We evaluate our solution with a realistic and 3GPP-compliantsimulator for factory automation use cases. Our simulation results confirm thatour solution can significantly decrease the training delay of the distributedAI service while keeping the URLLC availability above its required thresholdand close to the scenario where URLLC solely consumes all network resources.

Device Selection for the Coexistence of URLLC and Distributed Learning Services