With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

The trend of the next generation of the internet has already been scrutinized by top analytics
enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the …

Federated learning (FL) is a widely used distributed machine learning framework. However,
recent studies have shown its susceptibility to poisoning membership inference attacks …

Background The collection, storage, and analysis of large data sets are relevant in many
sectors. Especially in the medical field, the processing of patient data promises great …

In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

Abstract Machine learning has been applied in a wide range of various fields. To train more
effective control models, it is a trend for organizations holding their private data to …

Federated Learning (FL) aims to improve machine learning privacy by allowing several data
owners in edge and ubiquitous computing systems to collaboratively train a model, while …

Federated learning (FL) is recently surging as a promising decentralized deep learning (DL)
framework that enables DL-based approaches trained collaboratively across clients without …

Federated Learning (FL), a privacy-preserving training approach, has proven to be effective,
yet its vulnerability to attacks that extract information from model weights is widely …