In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

Data-driven machine learning (ML) has emerged as a promising approach for building
accurate and robust statistical models from medical data, which is collected in huge volumes …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

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

Federated learning enables a large amount of edge computing devices to jointly learn a
model without data sharing. As a leading algorithm in this setting, Federated Averaging …

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 …

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 …

Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep
neural networks in which participants' data remains on their own devices with only model …

Training deep neural networks requires gradient estimation from data batches to update
parameters. Gradients per parameter are averaged over a set of data and this has been …