Federated Learning (FL) is a well-known paradigm of distributed machine learning on
mobile and IoT devices, which preserves data privacy and optimizes communication …

The demand for effective, safe, and privacy-preserving machine learning methods has
increased due to the rapid growth of large pre-trained models in recent years. In large-scale …

Federated learning (FL) has received significant attention from both academia and industry,
as an emerging paradigm for building machine learning models in a communication-efficient …

The existing asynchronous federated learning methods have effectively addressed the issue
of low training efficiency in synchronous methods. However, due to the centralized trust …

The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent
healthcare systems, and various real-world applications have resulted in the generation of …

Federated learning is an emerging distributed machine learning paradigm, where multiple
data sources collaborate to train a model without sharing the original data, safeguarding …

In recent years, the explosion of big data has presented unparalleled opportunities for the
advancement of machine learning (ML). However, the vast size and sensitive nature of these …

Federated learning (FL), as a distributed machine learning paradigm, promotes personal
privacy by local data processing at each client. However, relying on a centralized server for …

Federated Learning (FL) is a distributed, and decentralized machine learning protocol. By
executing FL, a set of agents can jointly train a model without sharing their datasets with …

A random verification blockchain construction and privacy protection method for federated
learning was proposed to address the issues of local device model gradient leakage, the …