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.

通过优化混合矩阵，改进分散式学习的能源效率，该研究将问题建模为双层优化，其中底层通过图稀疏化求解，针对全连接基底拓扑提出了一种拥有保证性能的解决方案，针对一般情况提出了一种贪心启发式方法，并通过基于真实拓扑和数据集的模拟验证，结果表明该方案可以在保持训练模型质量的前提下，将最繁忙节点的能耗降低 54%-76%。

基于图稀疏化的高能效分散式学习

Energy-efficient Decentralized Learning via Graph Sparsification

We focus on causal discovery in the presence of measurement error in linear
systems where the mixing matrix, i.e., the matrix indicating the independent
exogenous noise terms pertaining to the observed variables, is identified up to
permutation and scaling of the columns. We demonstrate a somewhat surprising
connection between this problem and causal discovery in the presence of
unobserved parentless causes, in the sense that there is a mapping, given by
the mixing matrix, between the underlying models to be inferred in these
problems. Consequently, any identifiability result based on the mixing matrix
for one model translates to an identifiability result for the other model. We
characterize to what extent the causal models can be identified under a
two-part faithfulness assumption. Under only the first part of the assumption
(corresponding to the conventional definition of faithfulness), the structure
can be learned up to the causal ordering among an ordered grouping of the
variables but not all the edges across the groups can be identified. We further
show that if both parts of the faithfulness assumption are imposed, the
structure can be learned up to a more refined ordered grouping. As a result of
this refinement, for the latent variable model with unobserved parentless
causes, the structure can be identified. Based on our theoretical results, we
propose causal structure learning methods for both models, and evaluate their
performance on synthetic data.

探讨线性系统中存在测量误差时的因果推断问题，在鉴定列的置换和缩放范围内确定混合矩阵的情况下，发现这个问题与存在未观察到的无父原因的因果推断问题之间有相当惊人的联系，并提出了因果结构学习方法并在合成数据上评估了它们的性能。