Web: http://arxiv.org/abs/2209.10105

Sept. 22, 2022, 1:13 a.m. | Zhanhong Jiang, Aditya Balu, Xian Yeow Lee, Young M. Lee, Chinmay Hegde, Soumik Sarkar

stat.ML updates on arXiv.org arxiv.org

Regret has been widely adopted as the metric of choice for evaluating the
performance of online optimization algorithms for distributed, multi-agent
systems. However, data/model variations associated with agents can
significantly impact decisions and requires consensus among agents. Moreover,
most existing works have focused on developing approaches for (either strongly
or non-strongly) convex losses, and very few results have been obtained
regarding regret bounds in distributed online optimization for general
non-convex losses. To address these two issues, we propose a novel …

arxiv distributed optimization

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