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Published December 2019 | Published
Journal Article Open

Logarithmic Communication for Distributed Optimization in Multi-Agent Systems


Classically, the design of multi-agent systems is approached using techniques from distributed optimization such as dual descent and consensus algorithms. Such algorithms depend on convergence to global consensus before any individual agent can determine its local action. This leads to challenges with respect to communication overhead and robustness, and improving algorithms with respect to these measures has been a focus of the community for decades. This paper presents a new approach for multi-agent system design based on ideas from the emerging field of local computation algorithms. The framework we develop, LOcal Convex Optimization (LOCO), is the first local computation algorithm for convex optimization problems and can be applied in a wide-variety of settings. We demonstrate the generality of the framework via applications to Network Utility Maximization (NUM) and the distributed training of Support Vector Machines (SVMs), providing numerical results illustrating the improvement compared to classical distributed optimization approaches in each case.

Additional Information

© 2019 Association for Computing Machinery. OPen Access. Received August 2019; revised September 2019; accepted October 2019. This work was funded by the National Research Foundation through the AitF-1637598, CNS-1518941, and CNS-1254169 grants, along with the Linde Foundation and an Amazon AWS Artificial Intelligence Fellowship.

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August 22, 2023
August 22, 2023