Energy-eficient and federated meta-learning via projected stochastic gradient ascent
Elgabli, Anis; Ben Issaid, Chaouki; Bedi, Amrit S.; Bennis, Mehdi; Aggarwal, Vaneet (2022-02-02)
A. Elgabli, C. B. Issaid, A. S. Bedi, M. Bennis and V. Aggarwal, "Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent," 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685127
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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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