Artificial Intelligence (AI) has shown strong potential in supporting clinical decision-making through Clinical Decision Support Systems (CDSSs). However, ...

Abstract: Decentralized Federated Learning (DFL) enables collaborative model training without a central server but faces challenges in efficiency, stability, and trustworthiness due to communication ...

Sub-headline: HIT (Shenzhen) researchers develop FedPD to enhance personalized cross-architecture collaboration  Researchers ...

The up-coming technology such as Federated Learning will change the responsibility of storing personal data radically ...

Sub-headline: HIT (Shenzhen) researchers develop FedPD to enhance personalized cross-architecture collaboration   Researchers ...

“A scientific career is a journey of transfer learning and federated learning.” ...

AI Labs and Cybersecurity: Areas of Disruption and Limitations Introduction As cyber threats grow in complexity and frequency, organizations increasingly ...

The project sits at the intersection of privacy-preserving machine learning, distributed systems, and trustworthy AI, with implications for regulatory compliance and real-world deployment of federated ...

In an era where data breaches make headlines weekly and privacy regulations tighten globally, artificial intelligence faces a fundamental challenge: how to learn from data without compromising privacy ...

Abstract: Federated learning enables participants to collaboratively train a global model through distributed training without sharing raw data. However, this distributed training is vulnerable to ...

The study, titled “The Decentralized AI Ecosystem in Healthcare: A Systematic Review of Technologies, Governance, and ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...