Federated learning (FL) is a technique that allows multiple participants to collaboratively
train a Deep Neural Network (DNN) without the need to centralize their data. Among other …

Federated Learning (FL) offers decentralized machine learning (ML) capabilities while
potentially safeguarding data privacy. However, this architecture introduces unique security …

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

Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients
to collaboratively train a global model without sharing their local data. However, FL entails …

Federated learning is an emerging privacy-preserving distributed machine learning that
enables multiple parties to collaboratively learn a shared model while keeping each party's …

Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm enabling
multiple parties to train a model collaboratively without sharing their data. With the upcoming …

Ownership verification is currently the most critical and widely adopted post-hoc method to
safeguard model copyright. In general, model owners exploit it to identify whether a given …

Abstract The Internet of Things (IoT) is a rapidly growing technology that has been
generating increasing amounts of traffic from multiple devices. However, this growth in traffic …

Data is the cornerstone of using AI models, because their performance directly depends on
the diversity, quantity, and quality of the data used for training. Using AI presents unique …

Graph Neural Networks (GNNs) have significantly advanced various downstream graph-
relevant tasks, encompassing recommender systems, molecular structure prediction, social …