Abstract Generative Pre-trained Transformer (GPT) models have exhibited exciting progress
in their capabilities, capturing the interest of practitioners and the public alike. Yet, while the …

Federated learning (FL) refers to a system of training and stabilizing local machine learning
models at the global level by aggregating the learning gradients of the models. It reduces …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
collaborative training. Under domain skew the current FL approaches are biased and face …

Previous work on fairness in federated learning introduced the notion of* core stability*,
which provides utility-based fairness guarantees to any subset of participating agents …

In many real-world situations, data is distributed across multiple self-interested agents.
These agents can collaborate to build a machine learning model based on data from …

Federated learning (FL) provides a privacy-preserving approach for collaborative training of
machine learning models. Given the potential data heterogeneity, it is crucial to select …

Federated Learning (FL) aims to improve machine learning privacy by allowing several data
owners in edge and ubiquitous computing systems to collaboratively train a model, while …

Federated learning (FL) is decentralized machine learning framework that finds various
applications in health, finance, and the Internet of things. This article studies the under …

We revisit the recently developed framework of proportionally fair clustering, where the goal
is to provide group fairness guarantees that become stronger for groups of data points that …

Federated learning (FL) is a machine learning paradigm that allows multiple FL participants
(FL-PTs) to collaborate on training models without sharing private data. Due to data …