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 …

Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm
which narrows the application scenarios of FL and decreases the enthusiasm of data …

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made
revolutionary progress in recent years, and are widely used in various fields. The high …

In vehicular ad-hoc networks (VANET), federated learning enables vehicles to
collaboratively train a global model for intelligent transportation without sharing their local …

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 is a collaborative machine learning paradigm that allows training
models on decentralized data while preserving data privacy. It has gained significant …

With the development of the blockchain technology, a decentralized and de-trusted network
paradigm has been constructed, enabling multiple digital assets like NFT, to be permanently …

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 …

Federated learning (FL) models are constructed by multiple participants who provide their
training datasets and collaborate in joint training. However, training and deployment …

Federated learning (FL) emerges as an effective collaborative learning framework to
coordinate data and computation resources from massive and distributed clients in training …