Training Machine Learning models at the Edge: A Survey
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Edge-computingFederated LearningSurveyMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven …
BIG-bench Machine LearningPhysics-informed machine learningInformed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to…
BIG-bench Machine LearningGeneral ClassificationMachine Learning with Physics Knowledge for Prediction: A Survey
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted s…
Data AugmentationPhysics-informed machine learningPredictionSurveyCurriculum Graph Machine Learning: A Survey
Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random or…
Model OptimizationSurveyA Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
As machine learning technologies advance rapidly across various domains, concerns over data privacy and model security have grown significantly. These challenges are particularly pronounced when models are trained and de…
Privacy Preserving