Resource allocation and device pairing for energy-efficient NOMA-enabled federated edge learning

In the era of Fifth Generation of cellular networks (5G), Internet of Things (IoT) devices are becoming much more popular and accumulate a lot of data. With the advantages of privacy protection and resource saving, Federated Edge Learning (FEEL) is a promising machine learning paradigm which helps improve the service quality of deep learning-based applications and makes people's lives intelligent by processing these data. In FEEL, users only train the models locally without uploading their own data, thus alleviating the data security issue. Nevertheless, this paradigm also faces a major challenge. The training tasks are carried out on the mobile devices, which poses great pressure to their limited battery lives. In 5G, Non-Orthogonal Multiple Access (NOMA) has become an enabling technology to support massive connections because of its higher spectral efficiency and throughput over OMA technologies. Considering the potential of FEEL and the broad application prospects of NOMA, we design a NOMA-enabled FEEL framework and minimize its Energy Consumption (EC) by optimizing the device pairing, communication resource (i.e., transmission powers and time slots) and computation resource (i.e., CPU frequencies and local training threshold). The original problem can be decomposed into two subproblems: resource allocation and device pairing subproblems. For the resource allocation subproblem, with the help of the linear search method, we can find a near-optimal solution. Then, the optimality of the proposed solution (i.e., how far is the near-optimal from the optimum) is proved by sensitivity analysis. For the device pairing subproblem, we propose two low-complexity pairing schemes, which have their own characteristics. Simulation results demonstrate the effectiveness of the proposed resource allocation and device pairing strategies and highlight the advantage of the NOMA-enabled FEEL framework over the existing TDMA-enabled framework in terms of EC.