Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …

The success of machine learning (ML) techniques in the formerly difficult areas of data
analysis and pattern extraction has led to their widespread incorporation into various …

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

Error feedback (EF), also known as error compensation, is an immensely popular
convergence stabilization mechanism in the context of distributed training of supervised …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Federated learning is an important privacy-preserving multi-party learning paradigm,
involving collaborative learning with others and local updating on private data. Model …

Federated learning (FL) has attracted tremendous attentions in recent years due to its
privacy-preserving measures and great potential in some distributed but privacy-sensitive …

This paper studies the distributed federated learning problem when the exchanged
information between the server and the workers is quantized. A novel quantized federated …

Federated learning (FL) is a type of distributed machine learning approacs that trains global
models through the collaboration of participants. It protects data privacy as participants only …

Federated learning is a popular distributed machine learning paradigm that enables
collaborative model training at multiple entities via exchanging intermediate learning results …