Publication

Distributed Meta-Learning with Networked Agents

Ali H. Sayed, Stefan Vlaski, Mert Kayaalp
2021
Conference Papers

Abstract

Meta-learning aims to improve efficiency of learning new tasks by exploiting the inductive biases obtained from related tasks. Previous works consider centralized or federated architectures that rely on central processors, whereas, in this paper, we propose a decentralized meta-learning scheme where the data and the computations are distributed across a network of agents. We provide convergence results for non-convex environments and illustrate the theoretical findings with experiments.

Official source

https://infoscience.epfl.ch/entities/publication/3be6f112-303c-4b90-930d-c4bad1038484

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Distributed Meta-Learning with Networked Agents

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Topics in statistical physics of high-dimensional machine learning