Add to ORKGFederated Learning (FL), as an emerging form of distributed machine learning, can protect participants’ private data from being substantially disclosed to cyber adversaries. It has potential uses in many large-scale, data-rich environments, such as the Internet of Things (IoT), Industrial IoT, Social Media, and the emerging SM 3.0. However, federated learning is susceptible to some forms of data leakage through model inversion attacks. Such attacks occur through the analysis of participants’ uploaded model updates. Model inversion attacks can reveal private data and potentially undermine some critical reasons for employing federated learning paradigms. This paper proposes novel differential privacy (DP)-based deep federated learning framewo...
Federated learning enables data owners to jointly train a neural network without sharing their perso...
International audienceFederated learning becomes a prominent approach when different entities want t...
Data is coined to be the new oil due to the increasing awareness of its value in a myriad of applica...
AI\u27s applicability across diverse fields is hindered by data sensitivity, privacy concerns, and l...
Deep learning pervades heavy data-driven disciplines in research and development. The Internet of Th...
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keep...
Federated learning can combine a large number of scattered user groups and train models collaborativ...
As a popular distributed learning framework, federated learning (FL) enables clients to conduct coop...
With the increasing number of data collectors such as smartphones, immense amounts of data are avail...
The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individua...
Users are exposed to a large volume of harmful content that appears daily on various social network ...
Federated learning (FL) is an emerging technique that trains machine learning models across multiple...
Federated Learning (FL) is a paradigm for large-scale distributed learning which faces two key chall...
Federated learning (FL) enables multiple clients to jointly train a global learning model while keep...
Federated learning is an improved version of distributed machine learning that further offloads oper...
Federated learning enables data owners to jointly train a neural network without sharing their perso...
International audienceFederated learning becomes a prominent approach when different entities want t...
Data is coined to be the new oil due to the increasing awareness of its value in a myriad of applica...
AI\u27s applicability across diverse fields is hindered by data sensitivity, privacy concerns, and l...
Deep learning pervades heavy data-driven disciplines in research and development. The Internet of Th...
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keep...
Federated learning can combine a large number of scattered user groups and train models collaborativ...
As a popular distributed learning framework, federated learning (FL) enables clients to conduct coop...
With the increasing number of data collectors such as smartphones, immense amounts of data are avail...
The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individua...
Users are exposed to a large volume of harmful content that appears daily on various social network ...
Federated learning (FL) is an emerging technique that trains machine learning models across multiple...
Federated Learning (FL) is a paradigm for large-scale distributed learning which faces two key chall...
Federated learning (FL) enables multiple clients to jointly train a global learning model while keep...
Federated learning is an improved version of distributed machine learning that further offloads oper...
Federated learning enables data owners to jointly train a neural network without sharing their perso...
International audienceFederated learning becomes a prominent approach when different entities want t...
Data is coined to be the new oil due to the increasing awareness of its value in a myriad of applica...