FedDdrl: Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment

Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL. We consider the challenges of heterogeneous I...

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Detalles Bibliográficos
Autores principales: Wong, Yi Jie, Tham, Mau-Luen, Kwan, Ban-Hoe, Owada, Yasunori
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006882/
https://www.ncbi.nlm.nih.gov/pubmed/36904696
http://dx.doi.org/10.3390/s23052494

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006882/
https://www.ncbi.nlm.nih.gov/pubmed/36904696
http://dx.doi.org/10.3390/s23052494

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