Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

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 …

Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …

Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit
privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has …

Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …

Federated learning (FL) research has made progress in develo** algorithms for
distributed learning of global models, as well as algorithms for local personalization of those …

Federated learning is an emerging learning paradigm where multiple clients collaboratively
train a machine learning model in a privacy-preserving manner. Personalized federated …

Federated learning (FL) emerges as a decentralized learning framework which trains
models from multiple distributed clients without sharing their data to preserve privacy …

The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data
being produced at the edge. The integration of self-supervised learning (SSL) and federated …