Federated Learning Over Fully-Decoupled RAN Architecture for Two-Tier Computing Acceleration

Two-tier computing paradigm that takes full advantage of both the end-user and the cloud computation capabilities has emerged as a promising way to deal with computationally-intensive tasks in the next generation wireless networks. For promoting the integration of the two-tier computing, federated learning (FL) provides an effective framework to enable the collaboration between the end-user and the cloud. However, the key performance metric, i.e., FL training latency, will be severely affected by the worst wireless link quality in both uplink and downlink. In this paper, aiming at accelerating the FL enabled end-cloud two-tier computing over the wireless networks, we introduce the uplink and downlink fully-decoupled radio access network (FD-RAN) architecture to enhance the minimum wireless link rate via multiple base stations (BSs) access collaboration and power management solution. First, the Lagrange dual decomposition and the binary variable relaxation methods are leveraged to obtain an optimal multiple BS access scheme for the enhancement of minimum uplink and downlink SINR. Subsequently, we exploit the successive convex approximation (SCA) algorithm to deal with the uplink power control and downlink power allocation with a proved data rate lower bound. Furthermore, considering the dynamic channel realizations, a stochastic optimization technique with a convex surrogate function is utilized to find the best end-cloud two-tier computing scheme for FL applications. Simulation results have demonstrated the effectiveness of our proposed joint multiple access collaboration and power management solution over FD-RAN for achieving a faster FL enabled two-tier computing task.