Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph

no code implementations • 1 Oct 2022 • Yang Lu, Zhengxin Yu, Neeraj Suri

Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem.

Computational Efficiency Federated Learning +1

PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models

no code implementations • 13 Sep 2022 • William Hackett, Stefan Trawicki, Zhengxin Yu, Neeraj Suri, Peter Garraghan

Adversarial extraction attacks constitute an insidious threat against Deep Learning (DL) models in-which an adversary aims to steal the architecture, parameters, and hyper-parameters of a targeted DL model.

Adversarial Attack