  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.
