One of the crucial issues in federated learning is how to develop efficient optimization
algorithms. Most of the current ones require full device participation and/or impose strong …

Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be
trained across multiple decentralized devices. The selection of clients to participate in the …

Federated learning (FL) enables an effective and private distributed learning process.
However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The …

We develop a privacy-preserving distributed algorithm to minimize a regularized empirical
risk function when the first-order information is not available and data is distributed over a …

Federated Learning (FL) is a machine learning approach that enables private and
decentralized model training. Although FL has been shown to be very useful in several …

Federated learning has shown its advances recently but is still facing many challenges, such
as how algorithms save communication resources and reduce computational costs, and …

A fleet of connected vehicles easily produces many gigabytes of data per hour, making
centralized (off-board) data processing impractical. In addition, there is the issue of …

Federated learning (FL) has gained attention for enabling efficient distributed learning while
maintaining data privacy. However, the data privacy constraint reduces the transparency in …

We consider decentralized learning over a network of workers with heterogeneous datasets,
in the presence of Byzantine workers. Byzantine workers may transmit arbitrary or malicious …

Personalized decentralized learning aims to train individual personalized models for each
client to adapt to Non-IID data distributions and heterogeneous environments. However, the …