Energy-efficient Decentralized Learning via Graph Sparsification
Xusheng Zhang Cho-Chun Chiu Ting He
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.