You're currently viewing an old version of this dataset. To see the current version, click here.

Federated Learning for Internet of Things

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing.

Data and Resources

Cite this as

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor (2024). Dataset: Federated Learning for Internet of Things. https://doi.org/10.57702/cf63ufk1

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.1109/COMST.2021.3075439
Author Dinh C. Nguyen
More Authors
Ming Ding
Pubudu N. Pathirana
Aruna Seneviratne
Jun Li
H. Vincent Poor