Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping …
Federated learning (FL) has experienced a boom in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with emerging privacy …
J Zhu, J Cao, D Saxena, S Jiang, H Ferradi - ACM Computing Surveys, 2023 - dl.acm.org
Federated learning is a privacy-preserving machine learning technique that trains models across multiple devices holding local data samples without exchanging them. There are …
J Li, Y Shao, K Wei, M Ding, C Ma, L Shi… - … on Parallel and …, 2021 - ieeexplore.ieee.org
Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for …
Federated learning (FL) is a promising paradigm to realize distributed machine learning on heterogeneous clients without exposing their private data. However, there is the risk of …
Z Wang, Q Hu - arXiv preprint arXiv:2110.02182, 2021 - arxiv.org
With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life. However, issues of privacy and …
C Ma, J Li, L Shi, M Ding, T Wang… - IEEE Computational …, 2022 - ieeexplore.ieee.org
Motivated by the increasingly powerful computing capabilities of end-user equipment, and by the growing privacy concerns over sharing sensitive raw data, a distributed machine …
E Madill, B Nguyen, CK Leung, S Rouhani - Proceedings of the Fourth …, 2022 - dl.acm.org
Blockchain-based federated learning has gained significant interest over the last few years with the increasing concern for data privacy, advances in machine learning, and blockchain …
S Ji, J Zhang, Y Zhang, Z Han, C Ma - Future Generation Computer Systems, 2023 - Elsevier
Federated learning, as an emerging distributed machine learning technology, can use cross- device data to train a usable and secure shared model under the premise of protecting data …