Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation

Federated learning (FL) has found many successes in wireless communications; however, the implementation of FL has been hindered by the energy limitation of mobile devices (MDs) and the availability of training data at MDs. Wireless power transfer (WPT) and mobile crowdsensing (MCS) are promising technologies that can be leveraged to power energy-limited MDs and acquire data for learning tasks. How to integrate WPT and MCS toward sustainable FL solutions is a research topic entirely missing from the open literature. This work for the first time investigates a resource allocation problem in collaborative sensing-assisted sustainable FL (S2FL) networks with the goal of minimizing the total completion time. In particular, we investigate a practical harvesting-sensing-training-transmitting protocol in which energy-limited MDs first harvest energy from radio frequency signals, use it to gain a reward for user participation, sense the training data from the environment, train the local models at MDs, and transmit the model updates to the edge server. The total completion time minimization problem of jointly optimizing power transfer, transmit power allocation, data sensing, bandwidth allocation, local model training, and data transmission is complicated due to the nonconvex objective function, highly nonconvex constraints, and strongly coupled variables. In order to solve that problem, we apply the decomposition technique and develop a computationally efficient path-following algorithm to obtain the solution. In particular, inner convex approximations are developed for the resource allocation subproblem, and the subproblems are performed alternatively in an iterative fashion. Simulation results are provided to evaluate the effectiveness of the proposed S2FL algorithm in reducing the completion time up to 21.45% in comparison with other benchmark schemes. Further, we investigate an extension of our work from frequency-division multiple access (FDMA) to nonorthogonal multiple access (NOMA) and show that NOMA can speed up the total completion time 8.36% on average of the considered FL system.