マルチエージェント連合学習のための完全分散型スケーラブルガウス過程
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning
ADMMとコンセンサス法を用いて、複数エージェント間でガウス過程の学習と予測を完全分散・スケーラブルに行うアルゴリズムを提案し、数値実験で有効性を示した論文。
著者: George P. Kontoudis, Daniel J. Stilwell
分類: stat.ML, cs.LG, cs.MA, cs.RO, math.OC
原文アブストラクト
In this paper, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. To decentralize the implementation of GP training optimization algorithms, we employ the alternating direction method of multipliers (ADMM). A closed-form solution of the decentralized proximal ADMM is provided for the case of GP hyper-parameter training with maximum likelihood estimation. Multiple aggregation techniques for GP prediction are decentralized with the use of iterative and consensus methods. In addition, we propose a covariance-based nearest neighbor selection strategy that enables a subset of agents to perform predictions. The efficacy of the proposed methods is illustrated with numerical experiments on synthetic and real data.
