Federated Learning for Internet of Things

doi:doi:10.57702/cf63ufk1

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.

BibTex:

@dataset{Dinh_C_Nguyen_and_Ming_Ding_and_Pubudu_N_Pathirana_and_Aruna_Seneviratne_and_Jun_Li_and_H_Vincent_Poor_2024,
    abstract = {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.},
    author = {Dinh C. Nguyen and Ming Ding and Pubudu N. Pathirana and Aruna Seneviratne and Jun Li and H. Vincent Poor},
    doi = {10.57702/cf63ufk1},
    institution = {No Organization},
    keyword = {'Artificial Intelligence', 'Federated Learning', 'Internet of Things', 'Machine Learning'},
    month = {dec},
    publisher = {TIB},
    title = {Federated Learning for Internet of Things},
    url = {https://service.tib.eu/ldmservice/dataset/federated-learning-for-internet-of-things},
    year = {2024}
}