Energy-Efficient User-Edge Association and Resource Allocation for NOMA-Based Hierarchical Federated Learning: A Long-Term Perspective

Hierarchical Federated Learning (HFL) has been introduced to enhance the communication efficiency and scalability of traditional Federated Learning (FL). In addition, the integration of Non-Orthogonal Multiple Access (NOMA) into the HFL framework serves to bolster system capacity and spectral efficiency. However, the formidable challenge of energy efficiency persists, particularly in energy-constrained scenarios, which can be further compounded by factors such as Non-Independent and Identical Distribution (NIID) data, varying channels across users, heterogeneous computation and communication resources, and the interference from weak users. Motivated by this, we aim to minimize the sum of the computation and communication energy consumption of all users in the NOMA-based HFL system. This is achieved through a joint optimization of User-Edge Association (UEA) and Resource Allocation (RA). Specifically, we utilize Deep Reinforcement Learning (DRL) to optimize UEA to achieve the objective from a long-term perspective. Subsequently, computation and communication resources are jointly optimized by Newton's Method to balance the computation and communication energy consumption while meeting a given latency requirement. Numerical results show that our strategy significantly improves energy efficiency of the system compared with other benchmarks.