Federated learning (FL) has been developed as a promising framework to leverage the
resources of edge devices, enhance customers' privacy, comply with regulations, and …

Most of our lives are conducted in the cyberspace. The human notion of privacy translates
into a cyber notion of privacy on many functions that take place in the cyberspace. This …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

Recent studies show that private training data can be leaked through the gradients sharing
mechanism deployed in distributed machine learning systems, such as federated learning …

Secure aggregation consists of computing the sum of data collected from multiple sources
without disclosing these individual inputs. Secure aggregation has been found useful for …

Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

Federated Learning (FL), emerging as a distributed machine learning, is a popular paradigm
that allows multiple users to collaboratively train an intermediate model by exchanging local …

Federated learning was introduced to enable machine learning over large decentralized
datasets while promising privacy by eliminating the need for data sharing. Despite this, prior …

Federated learning (FL) has attracted growing interest for enabling privacy-preserving
machine learning on data stored at multiple users while avoiding moving the data off-device …

Privacy-preserving aggregation protocol is an essential building block in privacy-enhanced
federated learning (FL), which enables the server to obtain the sum of users' locally trained …