Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

Federated learning (FL) has emerged as a promising distributed learning paradigm with an
added advantage of data privacy. With the growing interest in collaboration among data …

In the healthcare field, cross-institutional collaboration can fasten medical research
progress. Vertical federated learning (VFL) addresses data heterogeneity across multiple …

This paper proposes a federated, fair, and fast k-means algorithm (F3KM) to solve the fair
clustering problem efficiently in scenarios where data cannot be shared among different …

Federated Learning (FL) methods often struggle in highly statistically heterogeneous
settings. Indeed, non-IID data distributions cause client drift and biased local solutions …

In industrial scenarios, cross-departmental collaboration is necessary to achieve quality
traceability. However, data cannot be shared due to privacy concerns. Vertical Federated …

In a distributed machine learning setting like Federated Learning where there are multiple
clients involved which update their individual weights to a single central server, often …

Federated Clustering (FC) is crucial to mining knowledge from unlabeled non-Independent
Identically Distributed (non-IID) data provided by multiple clients while preserving their …

Vertical federated learning (VFL) considers the case that the features of data samples are
partitioned over different participants. VFL consists of two main steps, ie, identify the …

Current Federated Learning (FL) methods often struggle with high statistical heterogeneity
across clients' data, resulting in client drift due to biased local solutions. This issue is …