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 …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Federated learning (FL) strives to enable collaborative training of machine learning models
without centrally collecting clients' private data. Different from centralized training, the local …

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 Learning (FL) is a distributed learning paradigm that can learn a global or
personalized model from decentralized datasets on edge devices. However, in the computer …

Federated learning allows multiple participants to collaboratively train an efficient model
without exposing data privacy. However, this distributed machine learning training method is …

We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems,
where a central server aggregates local models of distributed users, each of size, trained on …

We propose SwiftAgg, a novel secure aggregation protocol for federated learning systems,
where a central server aggregates local models of N distributed users, each of size L …

Federated learning (FL) has emerged as a practical solution to tackle data silo issues
without compromising user privacy. One of its variants, vertical federated learning (VFL), has …

Federated learning (FL) typically relies on synchronous training, which is slow due to
stragglers. While asynchronous training handles stragglers efficiently, it does not ensure …