Multi-frame Scheduling for Federated Learning over Energy-Efficient 6G Wireless Networks

It is envisioned that data-driven distributed learning approaches such as federated learning (FL) will be a key enabler for 6G wireless networks. However, the deployment of FL over wireless networks suffers from considerable energy consumption on communications and computation, which is challenging to meet stringent energy-efficiency goals of future sustainable 6G networks. In this paper, we investigate the energy consumption of transmitting scheduling decisions for FL deployed over a wireless network where mobile devices upload their local model to a coordinator (6G base station) for computing a global machine learning (ML) model iteratively. We consider that the coordinators have stringent energy efficiency goals. Therefore, to reduce the energy consumption due to the deployment of FL, we propose a novel multi-frame framework for FL that enables the coordinator to schedule wireless devices in one global round by only sending scheduling decisions at the beginning of each global round and setting the coordinator's transmission module to sleep mode to save power. In particular, we formulate a mixed-integer non-linear problem (MINLP) to minimize the average collection time of all device's local models by considering transmission errors. Then, we provide a novel method to solve the MINLP approximately and schedule wireless devices and allocate network resources. We demonstrate that our framework can save about 15 to 20 percent in some specific settings. Simulation results also show that our proposed algorithm outperforms traditional resource allocation methods and saves about 10% battery life per hundred global rounds in mobile device coordinators under certain scenarios.