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arXiv · 2105.14772

Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent

Anis Elgabli·Chaouki Ben Issaid·Amrit S. Bedi·Mehdi Bennis·Vaneet Aggarwal
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Abstract

In this paper, we propose an energy-efficient federated meta-learning framework. The objective is to enable learning a meta-model that can be fine-tuned to a new task with a few number of samples in a distributed setting and at low computation and communication energy consumption. We assume that each task is owned by a separate agent, so a limited number of tasks is used to train a meta-model. Assuming each task was trained offline on the agent's local data, we propose a lightweight algorithm that starts from the local models of all agents, and in a backward manner using projected stochastic gradient ascent (P-SGA) finds a meta-model. The proposed method avoids complex computations such as computing hessian, double looping, and matrix inversion, while achieving high performance at significantly less energy consumption compared to the state-of-the-art methods such as MAML and iMAML on conducted experiments for sinusoid regression and image classification tasks.

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Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi, Mehdi Bennis, Vaneet Aggarwal. 2021-05-31. Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent. https://arxiv.org/abs/2105.14772

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