… The first one is federated learning only requires the exchange of training updates. The other
… , and federated learning. We continue to demonstrate the decentralized privacy protection …

… and privacy applies equally well to FA use cases. This article provides a brief introduction
to key concepts in federated learning and analytics with an emphasis on how privacy …

… decentralized topology? Is there a better tradeoff between the convergence of FL and the
privacy … In this article, we devise a decentralized wireless privacypreserving federated learning (…

… a new decentralized aggregation algorithm for federated learning, focusing on the privacy
… We identified privacy weakness in the classical ADMM-based aggregation and introduced a …

… emerging distributed deep learning framework—federated learning, in which … federated
learning, such as privacy preservation and integrity verification. Furthermore, federated learning …

… We propose a decentralized federated learning scheme that simultaneously enhances
both privacy protection and trustworthiness against threats from untrusted nodes without …

… privacy-preserving decentralized FL for secure and efficient communication (FL-SEC) over
A-IoT. A novel blockchain structure is devised to achieve decentralized … the privacy of model …

… Decentralized Federated Learning In this chapter we present our method for decentralized
federated learning that aims at preservation of model and data privacy. Thereby, models that …

… Abstract—Conventional federated learning (FL) approaches generally rely on a centralized
… local differential privacy, we then propose a privacy-preserving and reliable decentralized FL …

… ’s data privacy. In this paper, we propose a communication-efficient scheme for decentralized
federated learning called ProxyFL, or proxy-based federated learning. Each participant in …