# D2D-Assisted Adaptive Federated Learning in Energy-Constrained Edge Computing
## 4 Partite Graph-Based FL Scheme with Adaptive D2D Pairing and Aperiodic Local Iterations
Making use of the time-varying impact of server computing power on model accuracy, we proposed an adaptive FL scheme with energy-aware D2D server pairing and aperiodic local training iterations in a partite graph theoretic approach. Moreover, we leveraged graph learning to exploit the performance gain of dataset correlation between servers in FL training, and we designed a graph-and-deep reinforcement learning-based server pairing algorithm, which further improves model aggregation efficiency. Finally, we evaluated the performance of the proposed algorithms through experimental simulations. The numerical results demonstrate that our designed FL schemes are promising, in terms of both training accuracy improvement and server energy saving compared with the benchmark algorithms. System model construction and problem formulation, Z.L. and Y.C. ; scheme design, K.Z., Y.Z., Y.C. and Y.L. ; performance evaluation, Y.Z. and K.Z. ; writing--original draft, K.Z. and Z.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key R&D Program of China under Grant 2021 YFB3202904. Not applicable. The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author. The authors declare no conflicts of interest. ## References

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