ACM Transactions on Intelligent Systems and Technology· 2026Q1
Training Machine Learning Models at the Edge: A Survey
- 8citations
- Q1SCImago
- 2026year
Short summary
This survey identifies federated learning as the dominant approach for optimizing Machine Learning (ML) model training at the edge, synthesizing existing knowledge and highlighting future trends.
AI-generated from the title and abstract; the full text is not read.
Key points
- Edge computing's potential for AI is hindered by a lack of focus on ML model training at the edge.
- Federated learning is the primary research focus for optimizing ML model training in edge environments.
- The survey offers guidelines for comparing edge learning optimization techniques and tools.
- It aims to provide a comprehensive overview of the current landscape and future directions in edge ML training.
AI-generated from the title and abstract; the full text is not read.
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 at the edge.
The authors' abstract, as published at the source. ACM Transactions on Intelligent Systems and Technology, 2026 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science