Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems

Multi-agent Systems


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Distributed machine learning (ML) is a modern computation paradigm that divides its workload into independent tasks that can be simultaneously achieved by multiple machines (i.e., agents) for better scalability. However, a typical distributed system is usually implemented with a central server that collects data statistics from multiple independent machines operating on different subsets of data to build a global analytic model. This centralized communication architecture however exposes a single choke point for operational failure and places severe bottlenecks on the server’s communication and computation capacities as it has to process a growing volume of communication from a crowd of learning agents. To mitigate these bottlenecks, this paper introduces a novel Collective Online Learning Gaussian Process framework for massive distributed systems that allows each agent to build its local model, which can be exchanged and combined efficiently with others via peer-to-peer communication to converge on a global model of higher quality. Finally, our empirical results consistently demonstrate the efficiency of our framework on both synthetic and real-world datasets.

Please cite our work using the BibTeX below.

  author    = {Trong Nghia Hoang and
               Quang Minh Hoang and
               Kian Hsiang Low and
               Jonathan P. How},
  title     = {Collective Online Learning via Decentralized Gaussian Processes in
               Massive Multi-Agent Systems},
  journal   = {CoRR},
  volume    = {abs/1805.09266},
  year      = {2018},
  url       = {},
  archivePrefix = {arXiv},
  eprint    = {1805.09266},
  timestamp = {Mon, 13 Aug 2018 16:49:12 +0200},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}

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