Distributed Online Learning with Multiple Kernels

Jeongmin Chae; Songnam Hong

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arXiv · 2102.12733

Distributed Online Learning with Multiple Kernels

Jeongmin Chae·Songnam Hong

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Abstract

We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion. Online learning is additionally assumed, where every learner receives continuous streaming data locally. This learning model is called a fully distributed online learning (or a fully decentralized online federated learning). For this model, we propose a novel learning framework with multiple kernels, which is named DOMKL. The proposed DOMKL is devised by harnessing the principles of an online alternating direction method of multipliers and a distributed Hedge algorithm. We theoretically prove that DOMKL over T time slots can achieve an optimal sublinear regret, implying that every learner in the network can learn a common function which has a diminishing gap from the best function in hindsight. Our analysis also reveals that DOMKL yields the same asymptotic performance of the state-of-the-art centralized approach while keeping local data at edge learners. Via numerical tests with real datasets, we demonstrate the effectiveness of the proposed DOMKL on various online regression and time-series prediction tasks.

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BibTeX RIS

Jeongmin Chae, Songnam Hong. 2021-02-26. Distributed Online Learning with Multiple Kernels. https://arxiv.org/abs/2102.12733

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