Recent advances in machine learning, wireless communication, and mobile hardware
technologies promisingly enable federated learning (FL) over massive mobile edge devices …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

To enable large-scale machine learning in bandwidth-hungry environments such as
wireless networks, significant progress has been made recently in designing communication …

This paper presents a novel collaborative generative modeling (CGM) framework that
incentivizes collaboration among self-interested parties to contribute data to a pool for …

The widespread adoption of deep learning is facilitated in part by the availability of large-
scale data for training desirable models. However, these data may involve sensitive …

We study federated learning (FL) with non-convex loss functions and data from people who
do not trust the server or other silos. In this setting, each silo (eg hospital) must protect the …

Decentralized Federated Learning (DeFL) plays a critical role in improving effectiveness of
training and has been proved to give great scope to the development of edge computing …

Differential private optimization for nonconvex smooth objective is considered. In the
previous work, the best known utility bound is $\widetilde O (\sqrt {d}/(n\varepsilon_\mathrm …

Abstract Machine learning models are increasingly used in high-stakes decision-making
systems. In such applications, a major concern is that these models sometimes discriminate …

This paper jointly considers privacy preservation and Byzantine-robustness in decentralized
learning. In a decentralized network, honest-but-curious agents faithfully follow the …