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 popular distributed learning framework to reduce
privacy risks by keeping private data locally. However, recent work (Hitaj 2017) has …

We propose a model-based reinforcement learning framework to derive untargeted
poisoning attacks against federated learning (FL) systems. Our framework first approximates …

With the advance in artificial intelligence and high-dimensional data analysis, federated
learning (FL) has emerged to allow distributed data providers to collaboratively learn without …

Federated learning (FL) provides an efficient paradigm to jointly train a global model
leveraging data from distributed users. As local training data comes from different users who …

Due to the rising awareness of privacy and security in machine learning applications,
federated learning (FL) has received widespread attention and applied to several areas, eg …

Federated learning (FL) allows untrusted clients to collaboratively train a common machine
learning model, called global model, without sharing their private/proprietary training data …

Federated learning is a secure machine learning technology proposed to protect data
privacy and security in machine learning model training. However, recent studies show that …

Connected and automated vehicles (CAVs) are becoming a reality. Prototyping and testing
of self-driving vehicle technology are becoming more popular around the world. The secure …

Federated learning is a novel distributed learning framework, where the deep learning
model is trained in a collaborative manner among thousands of participants. The shares …