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Logarithmic Communication for Distributed Optimization in Multi-Agent Systems

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Adam WiermanAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 3, Issue 3

Article No.: 48, Pages 1 - 29

https://doi.org/10.1145/3366696

Published: 17 December 2019 Publication History

Abstract

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.

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Cited By

Zhu WLi HDing NChen YSun YMa CLi W(2023)Virtual Target Based Multi-agent Surrounding Approach2023 19th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN60784.2023.00115(786-793)Online publication date: 14-Dec-2023
https://doi.org/10.1109/MSN60784.2023.00115
Wang LXu YXu HChen MHuang L(2022)Accelerating Decentralized Federated Learning in Heterogeneous Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2022.3178378(1-1)Online publication date: 2022
https://doi.org/10.1109/TMC.2022.3178378
Zhu ZZhu JLiu JLiu Y(2021)Federated BanditProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34473805:1(1-29)Online publication date: 22-Feb-2021
https://dl.acm.org/doi/10.1145/3447380

Index Terms

Logarithmic Communication for Distributed Optimization in Multi-Agent Systems
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems
  2. Distributed computing methodologies
    1. Distributed algorithms
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization
        Convex optimization

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 3, Issue 3

SIGMETRICS

December 2019

525 pages

EISSN:2476-1249

DOI:10.1145/3376928

Editors:
Augustin Chaintreau
Columbia University
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Leana Golubchik
University of Southern California
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Zhi-Li Zhang
University of Minnesota

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Published: 17 December 2019

Published in POMACS Volume 3, Issue 3

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Cited By

Zhu WLi HDing NChen YSun YMa CLi W(2023)Virtual Target Based Multi-agent Surrounding Approach2023 19th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN60784.2023.00115(786-793)Online publication date: 14-Dec-2023
https://doi.org/10.1109/MSN60784.2023.00115
Wang LXu YXu HChen MHuang L(2022)Accelerating Decentralized Federated Learning in Heterogeneous Edge ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2022.3178378(1-1)Online publication date: 2022
https://doi.org/10.1109/TMC.2022.3178378
Zhu ZZhu JLiu JLiu Y(2021)Federated BanditProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34473805:1(1-29)Online publication date: 22-Feb-2021
https://dl.acm.org/doi/10.1145/3447380

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