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Energy-efficient Decentralized Learning via Graph Sparsification
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Energy-efficient Decentralized Learning via Graph Sparsification
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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.
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Cited by 1 Pith paper
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Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning
A multi-phase randomized mixing-matrix schedule reduces worst-case per-node energy in decentralized federated learning, with convergence analysis for time-varying communication topologies.
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