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
Este estudio examina el campo dinámico del Aprendizaje Automático Distribuido (DML) en el contexto de la Computación en el Borde (EC). Analiza las arquitecturas predominantes, la implementación, identifica desafíos críticos y sintetiza técnicas de mitigación propuestas dentro de entornos de borde con recursos limitados. El estudio delimita arquitecturas Edge-Only y Cloud-Edge, así como la implementación de Aprendizaje Federado (FL), destacando sus características y su idoneidad para diversas aplicaciones dentro de DML. Examina exhaustivamente desafíos fundamentales, incluidos las limitaciones de recursos, la eficiencia energética, la sobrecarga de comunicación, la privacidad de los datos, la resiliencia a fallos y la heterogeneidad de los datos. Al explorar estrategias recientes, el estudio proporciona una visión integral de las soluciones actuales y de las prometedoras direcciones futuras de investigación para optimizar el despliegue de DML en el borde de la red.
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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
