  Recently, 6G in-X subnetworks have been proposed as low-power short-range
radio cells to support localized extreme wireless connectivity inside entities
such as industrial robots, vehicles, and the human body. Deployment of in-X
subnetworks within these entities may result in rapid changes in interference
levels and thus, varying link quality. This paper investigates distributed
dynamic channel allocation to mitigate inter-subnetwork interference in dense
in-factory deployments of 6G in-X subnetworks. This paper introduces two new
techniques, Federated Multi-Agent Double Deep Q-Network (F-MADDQN) and
Federated Multi-Agent Deep Proximal Policy Optimization (F-MADPPO), for channel
allocation in 6G in-X subnetworks. These techniques are based on a
client-to-server horizontal federated reinforcement learning framework. The
methods require sharing only local model weights with a centralized gNB for
federated aggregation thereby preserving local data privacy and security.
Simulations were conducted using a practical indoor factory environment
proposed by 5G-ACIA and 3GPP models for in-factory environments. The results
showed that the proposed methods achieved slightly better performance than
baseline schemes with significantly reduced signaling overhead compared to the
baseline solutions. The schemes also showed better robustness and
generalization ability to changes in deployment densities and propagation
parameters.
