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Training Machine Learning models at the Edge: A Survey

Aymen Rayane Khouas, Mohamed Reda Bouadjenek, Hakim Hacid, Sunil Aryal

TL;DR

This survey explores the concept of edge learning, specifically the optimization of ML model training at the edge, and provides a guideline for comparing techniques used to optimize ML for edge learning, along with an exploration of the different frameworks, libraries, and simulation tools available.

Abstract

Edge computing has gained significant traction in recent years, promising enhanced efficiency by integrating artificial intelligence capabilities at the edge. While the focus has primarily been on the deployment and inference of Machine Learning (ML) models at the edge, the training aspect remains less explored. This survey, explores the concept of edge learning, specifically the optimization of ML model training at the edge. The objective is to comprehensively explore diverse approaches and methodologies in edge learning, synthesize existing knowledge, identify challenges, and highlight future trends. Utilizing Scopus and Web of science advanced search, relevant literature on edge learning was identified, revealing a concentration of research efforts in distributed learning methods, particularly federated learning. This survey further provides a guideline for comparing techniques used to optimize ML for edge learning, along with an exploration of the different frameworks, libraries, and simulation tools available. In doing so, the paper contributes to a holistic understanding of the current landscape and future directions in the intersection of edge computing and machine learning, paving the way for informed comparisons between optimization methods and techniques designed for training on the edge.

Training Machine Learning models at the Edge: A Survey

TL;DR

This survey explores the concept of edge learning, specifically the optimization of ML model training at the edge, and provides a guideline for comparing techniques used to optimize ML for edge learning, along with an exploration of the different frameworks, libraries, and simulation tools available.

Abstract

Edge computing has gained significant traction in recent years, promising enhanced efficiency by integrating artificial intelligence capabilities at the edge. While the focus has primarily been on the deployment and inference of Machine Learning (ML) models at the edge, the training aspect remains less explored. This survey, explores the concept of edge learning, specifically the optimization of ML model training at the edge. The objective is to comprehensively explore diverse approaches and methodologies in edge learning, synthesize existing knowledge, identify challenges, and highlight future trends. Utilizing Scopus and Web of science advanced search, relevant literature on edge learning was identified, revealing a concentration of research efforts in distributed learning methods, particularly federated learning. This survey further provides a guideline for comparing techniques used to optimize ML for edge learning, along with an exploration of the different frameworks, libraries, and simulation tools available. In doing so, the paper contributes to a holistic understanding of the current landscape and future directions in the intersection of edge computing and machine learning, paving the way for informed comparisons between optimization methods and techniques designed for training on the edge.
Paper Structure (60 sections, 7 figures, 3 tables)

This paper contains 60 sections, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Comparison with existing surveys
  3. Structure of the survey
  4. Edge computing and Edge learning
  5. Edge Computing
  6. Edge Learning
  7. Requirements for edge learning
  8. Overview of Edge Learning techniques
  9. Distributed and Collaborative Techniques
  10. Federated Learning
  11. Split Learning
  12. Other Collaborative Learning methods
  13. Adaptive and Fine-tuning based Techniques
  14. Transfer Learning
  15. Incremental Learning
  16. ...and 45 more sections

Figures (7)

  • Figure 1: A typical architecture of edge computing
  • Figure 2: A taxonomy of techniques used to enable and/or optimize edge learning
  • Figure 3: Trend of techniques used to train ML models in the edge over the years
  • Figure 4: Trend of different techniques used hand in hand for training ML models at the edge. Color intensity represents the number of papers
  • Figure 5: Unsupervised learning with the training happening only on a single device, with all the required data hosted locally
  • ...and 2 more figures