# D2D-Assisted Adaptive Federated Learning in Energy-Constrained Edge Computing
## 2 Related Work  
Federated learning, as a promising distributed and privacy-preserving machine learning technology, has been explored and applied in various fields. In [7], the authors used FL to exploit the parallel-processing power of multiple servers for training mobile traffic-prediction models, and they devised a server-selection-and-dataset-management scheme for FL participation. In [8], the authors applied FL in radio frequency fingerprinting, and they developed a deep federated learning network, which facilitated distributed fingerprinting learning while ensuring privacy preservation within diverse networks. In [9], FL was utilized for resource allocation and device orchestration in non-orthogonal multiple-access-enabled industrial IoT networks, where a hierarchical FL framework was presented to reduce learning overheads. In [10], the authors designed an FL-based distributed algorithm to predict file popularity for edge caching arrangement, which solved user privacy issues in centralized approaches. With the rise of the new generation of wireless communications, FL has been used for a variety of radio automating, network slicing, and process orchestration. The authors in [11] comprehensively investigated FL-empowered mobile network management from the access to the core of 5G-beyond networks. The authors in [12] applied FL to the medical field, and they developed a privacy infrastructure based on federated learning and blockchain to provide a shared model of the spread of the COVID-19 virus. As the distributed processing pattern of FL well-matches the multi-server architecture of MEC, the integration of FL and MEC has been extensively studied recently. For example, in [13] the authors introduced a clustered FL for mobile traffic-prediction MEC systems. By clustering the MEC servers based on their geographical location and data patterns, the learning straggler impact caused by the heterogeneity of the MEC servers was alleviated. The authors in [14] focused on the slow convergence of FL caused by learning-community changes, and they proposed a meta-learning-aided FL, where new clients could learn from other ones and quickly perform learning scene migration. In order to obtain accurate machine learning models for time-sensitive applications, the authors in [15] presented an age-aware FL framework, and they reduced the age of the data in MEC networks through data selection and aggregator placement. Considering that model parameters may be lost due to unreliable communication, thereby slowing down the FL training process, the authors in [2] devised a multi-layer computing architecture with dynamic epoch adjustment in FL-enabled MEC systems. To tackle management complexity caused by the continuous growth of user equipment, the authors in [16] proposed a multiple-layer framework with clustered clients to deploy FL algorithms on large-scale MEC networks. In [17], the authors aimed to address the problem that FL may malfunction in MEC systems, due to device heterogeneity, unstable channels, and unpredictable user mobility, and they proposed a multi-core FL framework to help FL algorithms to be implemented in MEC systems. In [18], the authors focused on the incentives for clients participating in FL, and they designed an incentive mechanism that selected high-quality edge nodes to contribute to FL training through a heuristic algorithm. In order to reduce the energy consumption caused by parameter transmission in the FL training process, the authors in [19] proposed an energy-and-distribution-aware collaborative-clustering algorithm to select multiple IoT devices with sufficient energy as aggregation cluster heads. The authors in [20] developed a weight-quantization scheme to facilitate on-device local training over heterogeneous mobile devices. A mixed-integer programming problem was formulated to jointly determine quantization and spectrum allocation to minimize overall FL energy consumption.