Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly
developing in an era where privacy protection is increasingly valued. It is this rapid …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

Federated Learning (FL) has been widely accepted as the solution for privacy-preserving
machine learning without collecting raw data. While new technologies proposed in the past …

Federated Learning (FL) is a promising paradigm that offers significant advancements in
privacy-preserving, decentralized machine learning by enabling collaborative training of …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

Federated Learning (FL) has become an established technique to facilitate privacy-
preserving collaborative training. However, new approaches to FL often discuss their …

IoT devices produce a wealth of data desired for learning models to empower more
intelligent applications. However, such data is often privacy sensitive making data owners …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …