A recent take toward federated analytics (FA), which allows analytical insights of distributed
data sets, reuses the federated learning (FL) infrastructure to evaluate the summary of model …

Federated learning aims to train a machine learning model (eg, a neural network) in a data-
decentralized fashion. The key challenge is the potential data heterogeneity among clients …

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

Clustering clients into groups that exhibit relatively homogeneous data distributions
represents one of the major means of improving the performance of federated learning (FL) …

Many enterprise solutions can greatly benefit from Machine Learning (ML) models that are
created from cross-domain enterprise data. However, many enterprises cannot share data …

Abstract In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train
Machine Learning (ML) models across the participants of a federation while preserving data …

Federated Learning (FL) has gained considerable attention recently, as it allows clients to
cooperatively train a global machine learning model without sharing raw data. However, its …

Federated learning (FL) inevitably confronts the challenge of system heterogeneity in
practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in …

Federated learning is an emerging technique for training models from decentralized data
sets. In many applications, data owners participating in the federated learning system hold …