This work aims at improving the energy efficiency of decentralized learning by optimizing the mixing matrix, which controls the communication demands during the learning process. Through rigorous analysis based on a state-of-the-art decentralized learning algorithm, the problem is formulated as a bi-level optimization, with the lower level solved by graph sparsification. A solution with guaranteed performance is proposed for the special case of fully-connected base topology and a greedy heuristic is proposed for the general case. Simulations based on real topology and dataset show that the proposed solution can lower the energy consumption at the busiest node by 54%-76% while maintaining the quality of the trained model.

翻译：本研究旨在通过优化控制学习过程中通信需求的混合矩阵，提升去中心化学习的能源效率。基于最先进的去中心化学习算法进行严格分析后，将该问题建模为双层优化问题，其中下层问题通过图稀疏化求解。针对全连接基础拓扑这一特例，提出了一种具有性能保证的解决方案，并针对一般情形给出了贪心启发式算法。基于真实拓扑和数据集的仿真结果表明，所提方案可将最繁忙节点的能耗降低54%-76%，同时保持训练模型的质量。