X Yin, Y Zhu, J Hu - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the …
Federated learning is a machine learning paradigm that emerges as a solution to the privacy- preservation demands in artificial intelligence. As machine learning, federated learning is …
Over the last decade, smart cities (SC) have been developed worldwide. Implementing big data and the internet of things improves the monitoring and integration of different …
L Zhang, J Xu, P Vijayakumar… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this work, the federated learning mechanism is introduced into the deep learning of medical models in Internet of Things (IoT)-based healthcare system. Cryptographic …
Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by …
Z Liu, J Guo, W Yang, J Fan, KY Lam… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …
Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of …
Abstract Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the …
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the …