Distributed Machine Learning on Edge Computing: A Survey of Challenges and Techniques
DOI:
https://doi.org/10.5753/reic.2026.8089Keywords:
Distributed Machine Learning, Federated Learning, Edge Computing, Edge Intelligence, Resource-Constrained EnvironmentsAbstract
This survey examines the dynamic field of Distributed Machine Learning (DML) in the context of Edge Computing (EC). It analyzes prevailing architectures, implementation, identifies critical challenges, and synthesizes proposed mitigation techniques within resource-constrained edge environments. The study delineates Edge-Only and Cloud-Edge architectures, as well as Federated Learning (FL) implementation, highlighting their characteristics and suitability for diverse applications within DML. It thoroughly examines fundamental challenges, including resource limitations, energy efficiency, communication overhead, data privacy, failure resilience, and data heterogeneity. By exploring recent strategies, the survey provides a comprehensive overview of current solutions and promising future research directions for optimizing DML deployment at the network edge.
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