The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …

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 …

Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning
paradigm that aggregates user-trained local gradients into a federated model through a …

Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …

Federated learning (FL) enables multiple clients to jointly train high-performance deep
learning models while maintaining the training data locally. However, it is challenging to …

With more regulations tackling the protection of users' privacy-sensitive data in recent years,
access to such data has become increasingly restricted. A new decentralized training …

In recent years, privacy concerns have become a serious issue for companies wishing to
protect economic models and comply with end-user expectations. In the same vein, some …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …