Objectives Federated learning (FL) has gained popularity in clinical research in recent years
to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent …

In federated learning (FL), multiple clients collaborate to train machine learning models
together while keeping their data decentralized. Through utilizing more training data, FL …

With decentralized data collected from diverse clients, a personalized federated learning
paradigm has been proposed for training machine learning models without exchanging raw …

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 …

Most existing works on federated bandits take it for granted that all clients are altruistic about
sharing their data with the server for the collective good whenever needed. Despite their …

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 is a machine learning protocol that enables a large population of agents
to collaborate over multiple rounds to produce a single consensus model. There are several …

Collaborations among various entities, such as companies, research labs, AI agents, and
edge devices, have become increasingly crucial for achieving machine learning tasks that …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed
learning framework. In this work, we focus on cross-silo FL, where clients become the model …