Recently, innovations in the Internet of Medical Things (IoMT), information and communication technologies, and machine learning (ML) have enabled smart healthcare …
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to learn collaboratively without sharing their private data. However, excessive computation and …
Federated learning (FL) has attracted growing attentions via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume …
As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …
Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing to the advantages of federated learning, federated graph learning (FGL) enables clients to …
KN Kumar, CK Mohan… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Federated learning (FL) has emerged as a powerful machine learning technique that enables the development of models from decentralized data sources. However, the …
The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual benefits, presents a unique opportunity to unlock new possibilities in AI research, and …
Classic black-box adversarial attacks can take advantage of transferable adversarial examples generated by a similar substitute model to successfully fool the target model …
With the rapid development of low-cost consumer electronics and pervasive adoption of next generation wireless communication technologies, a tremendous amount of data has been …