In this paper, we develop a privacy-preserving decentralized aggregation protocol for
federated learning. We formulate the distributed aggregation protocol with the Alternating …

Federated learning has emerged as a promising paradigm by collaboratively training a
global model through sharing local gradients without exposing raw data. However, the …

Federated learning has emerged as a promising approach for collaborative and privacy-
preserving learning. Participants in a federated learning process cooperatively train a model …

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 …

Decentralized federated learning (DFL) is an emerging privacy-preserving machine learning
framework, where multiple data owners cooperate to train a global model without any …

Secure aggregation is a popular protocol in privacy-preserving federated learning, which
allows model aggregation without revealing the individual models in the clear. On the other …

Federated learning is the distributed machine learning framework that enables collaborative
training across multiple parties while ensuring data privacy. Practical adaptation of XGBoost …

Federated learning has been spotlighted as a way to train neural networks using distributed
data with no need for individual nodes to share data. Unfortunately, it has also been shown …

Secure aggregation is a critical component in federated learning (FL), which enables the
server to learn the aggregate model of the users without observing their local models …

Federated machine learning leverages edge computing to develop models from network
user data, but privacy in federated learning remains a major challenge. Techniques using …