University of Oulu

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

Energy-eficient and federated meta-learning via projected stochastic gradient ascent

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Author: Elgabli, Anis1; Ben Issaid, Chaouki1; Bedi, Amrit S.2;
Organizations: 1Centre for Wireless Communications (CWC) University of Oulu, Finland
2US Army Research Laboratory, MD, USA
3School of Electrical and Computer Engineering, Purdue University
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: IEEE, 2022
Publish Date: 2023-03-27


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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ISBN: 978-1-7281-8104-2
ISBN Print: 978-1-7281-8105-9
DOI: 10.1109/GLOBECOM46510.2021.9685127
Host publication: 2021 IEEE Global Communications Conference (GLOBECOM)
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is supported by Academy of Finland 6G Flagship (grant no.n318927) and project SMARTER, projects EU-ICT IntellIoT and EUCHIS-TERA LearningEdge, and CONNECT, Infotech-NOOR, and NEGEI.
EU Grant Number: (957218) IntellIoT - Intelligent, distributed, human-centered and trustworthy IoT environments
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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