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 …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Gradient inversion attack (or input recovery from gradient) is an emerging threat to the
security and privacy preservation of Federated learning, whereby malicious eavesdroppers …

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

Recent studies show that private training data can be leaked through the gradients sharing
mechanism deployed in distributed machine learning systems, such as federated learning …

Federated learning (FL) is a rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …

Passing gradient is a widely used scheme in modern multi-node learning system (eg,
distributed training, collaborative learning). In a long time, people used to believe that …

Federated Learning (FL) is a distributed learning framework, in which the local data never
leaves clients' devices to preserve privacy, and the server trains models on the data via …

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 …