New Paper Accepted
Our paper Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent, joint work with Anis Elgabli, Amrit S. Bedi, Mehdi Bennis and Vaneet Aggarwal, has been accepted in 2021 IEEE Global Communications Conference (GLOBECOM).
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 stateof-the-art methods such as MAML and iMAML on conducted experiments for sinusoid regression and image classification tasks