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 models are collaboratively developed upon valuable training data
owned by multiple parties. During the development and deployment of federated models …

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 …

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 …

Computer Vision (CV) is playing a significant role in transforming society by utilizing
machine learning (ML) tools for a wide range of tasks. However, the need for large-scale …

Federated learning (FL) allows multiple parties to cooperatively learn a federated model
without sharing private data with each other. The need of protecting such federated models …

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

In decentralized federated learning (DFL), clients exchange their models with each other for
global aggregation. Due to a lack of centralized supervision, a client may easily duplicate …

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 …