Distributed Meta-Learning with Networked Agents
Mert Kayaalp, Stefan Vlaski, Ali H. Sayed · 2021 29th European Signal Processing Conference (EUSIPCO) · 2021
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.