The rapid emergence of massive datasets in various fields poses a serious challenge to
traditional statistical methods. Meanwhile, it provides opportunities for researchers to …

With the proliferation of training data, distributed machine learning (DML) is becoming more
competent for large-scale learning tasks. However, privacy concerns have to be given …

Distributed machine learning allows different parties to learn a single model over all data
sets without disclosing their own data. In this paper, we propose a weighted distributed …

As the implementation of machine learning (ML) systems becomes more widespread,
especially with the introduction of larger ML models, we perceive a spring demand for …

Ambient intelligence demands collaboration schemes for distributed constrained devices
which are not only highly energy efficient in distributed sensing, processing and …

In this paper, we propose a novel and efficient privacy-preserving split federated learning
(PPSFL) framework, that achieves both privacy protection and model accuracy with …

Distributed learning is a very effective divide-and-conquer strategy for dealing with big data.
As distributed learning algorithms become more and more mature, network security issues …

Privacy is the main concern in cyberspace because, every single click of a user on Internet is
recognized and analyzed for different purposes like credit card purchase records, healthcare …

In this paper, we consider the privacy-preserving problem in collaborative computing. Based
on a two-step average computation framework, we propose three privacy-aware schemes …

Distributed learning is a very effective divide-and-conquer strategy for dealing with big data.
As distributed learning algorithms become more and more mature, network security issues …