With the increasing attention on Machine Learning applications, more and more companies are involved in implementing AI components into their software products in order to improve …
Since its inception in 2016, federated learning has evolved into a highly promising decentral- ized machine learning approach, facilitating collaborative model training across numerous …
T Yin, L Li, W Lin, D Ma, Z Han - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
In recent years, federated learning (FL) plays an important role in data privacy-sensitive scenarios to perform learning works collectively without data exchange. However, due to the …
Federated learning has received attention for its efficiency and privacy benefits, in settings where data is distributed among devices. Although federated learning shows significant …
Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing …
Z Zhao, Y Mao, Z Shi, Y Liu, T Lan… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
The widespread adoption of Federated Learning (FL), a privacy-preserving distributed learning methodology, has been impeded by the challenge of high communication …
H Wang, Z Kaplan, D Niu, B Li - IEEE INFOCOM 2020-IEEE …, 2020 - ieeexplore.ieee.org
The widespread deployment of machine learning applications in ubiquitous environments has sparked interests in exploiting the vast amount of data stored on mobile devices. To …
L Shanmugam, R Tillu, M Tomar - Journal of Knowledge Learning and …, 2023 - jklst.org
Federated learning has emerged as a promising paradigm in the domain of distributed artificial intelligence (AI) systems, enabling collaborative model training across …
With an increasing number of smart devices like internet of things devices deployed in the field, offloading training of neural networks (NNs) to a central server becomes more and …