An Adaptive Communication‐Efficient Federated Learning to Resist Gradient‐Based Reconstruction Attacks

Y Li, Y Li, H Xu, S Ren - Security and Communication Networks, 2021 - Wiley Online Library
The widely deployed devices in Internet of Things (IoT) have opened up a large amount of
IoT data. Recently, federated learning emerges as a promising solution aiming to protect …

[Retracted] Defending Privacy Inference Attacks to Federated Learning for Intelligent IoT with Parameter Compression

Y Zhu, H Cao, Y Ren, W Wang, B Wang… - Security and …, 2023 - Wiley Online Library
Federated learning has been popularly studied with people's increasing awareness of
privacy protection. It solves the problem of privacy leakage by its ability that allows many …

MemDefense: Defending against Membership Inference Attacks in IoT-based Federated Learning via Pruning Perturbations

M Shen, J Meng, K Xu, S Yu… - IEEE Transactions on Big …, 2024 - ieeexplore.ieee.org
Depending on large-scale devices, the Internet of Things (IoT) provides massive data
support for resource sharing and intelligent decision, but privacy risks also increase. As a …

Lightweight federated learning for large-scale IoT devices with privacy guarantee

Z Wei, Q Pei, N Zhang, X Liu, C Wu… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
With the massive deployment of the Internet of Things (IoT) devices, many data analysis
applications emerge for the large amount of data accumulated by IoT. Federated learning …

Efficient Privacy-Preserving Federated Learning Against Inference Attacks for IoT

Y Miao, S Chen - 2023 IEEE Wireless Communications and …, 2023 - ieeexplore.ieee.org
Based on the vulnerability of federated learning (FL) to inference attacks and the high
computation overhead, lack of label protection and degraded model performance occurred …

Federated Learning for IoT Applications, Attacks and Defense Methods

Z Qu, R Duan, Y Liu, Z Lu - AI Embedded Assurance for Cyber Systems, 2023 - Springer
Federated Learning (FL) is a distributed Machine Learning (ML) framework, which has been
considered as a reliable approach to preserve privacy for the widely used Internet of Things …

SPEFL: efficient security and privacy enhanced federated learning against poisoning attacks

Z Ke, L Shen, J Shi, X Zhang, Y Sun… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Federated learning (FL) is a distributed machine learning paradigm in the Internet of Things
(IoT), which allows multiple devices to collaboratively train models without leaking local …

High-accuracy low-cost privacy-preserving federated learning in IoT systems via adaptive perturbation

T Liu, X Hu, H Xu, T Shu, DN Nguyen - Journal of Information Security and …, 2022 - Elsevier
With the rapid development of the Internet of Things (IoT), federated learning (FL) has been
widely used to obtain insights from collected data while preserving data privacy. Differential …

Accountable and verifiable secure aggregation for federated learning in IoT networks

X Yang, Y Zhao, Q Chen, Y Yu, X Du… - IEEE Network, 2022 - ieeexplore.ieee.org
In the Internet of things (IoT) networks, largescale IoT devices are connected to the Internet
to collect users' data. As a distributed machine learning paradigm, federated learning (FL) …

[HTML][HTML] Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions

JPA Yaacoub, HN Noura, O Salman - Internet of Things and Cyber-Physical …, 2023 - Elsevier
Abstract Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a
reputation for not only building Machine Learning (ML) models that rely on distributed …