Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications
Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications
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DOI：
10.1109/access.2023.3284891
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发表时间：
2023-06
期刊：
影响因子：
3.9
通讯作者：
Ege C. Kaya;M. Sahin;Abolfazl Hashemi
中科院分区：
文献类型：
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作者：
Ege C. Kaya;M. Sahin;Abolfazl Hashemi
本文研究了一个多智能体零阶在线优化问题，在联邦学习设置目标跟踪。智能体只感知它们与目标的当前距离，并旨在保持彼此之间的最小安全距离以防止碰撞。代理之间的协调和碰撞预防信息的传播是由一个中央服务器使用联邦学习范式管理。所提出的配方导致分布式在线非凸优化问题，通过一组通信受限的代理解决的一个实例。为了处理代理的通信限制，基于错误反馈的压缩方案被用于代理到服务器的通信。针对一般的分布式在线非凸优化问题，对该算法进行了理论分析。我们提供的非渐近收敛速度，显示占主导地位的长期是独立的压缩方案的特点。我们的理论结果采用了一种新的方法，采用更宽松的假设相比，标准文献。所提出的解决方案的性能进一步分析数值方面的跟踪误差和代理之间的碰撞在两个相关的应用程序。
This paper focuses on a multi-agent zeroth-order online optimization problem in a federated learning setting for target tracking. The agents only sense their current distances to their targets and aim to maintain a minimum safe distance from each other to prevent collisions. The coordination among the agents and dissemination of collision-prevention information is managed by a central server using the federated learning paradigm. The proposed formulation leads to an instance of distributed online nonconvex optimization problem that is solved via a group of communication-constrained agents. To deal with the communication limitations of the agents, an error feedback-based compression scheme is utilized for agent-to-server communication. The proposed algorithm is analyzed theoretically for the general class of distributed online nonconvex optimization problems. We provide non-asymptotic convergence rates that show the dominant term is independent of the characteristics of the compression scheme. Our theoretical results feature a new approach that employs significantly more relaxed assumptions in comparison to standard literature. The performance of the proposed solution is further analyzed numerically in terms of tracking errors and collisions between agents in two relevant applications.