TL;DR
A hybrid deep learning (DL) based privacy-preserving data system using Federated Learning (FL) for IIoT that achieves accuracies of 99.9% and 99.93%, respectively, while maintaining lower storage and computational overhead compared to existing approaches.
The concept of Federated Learning (FL) allows training models in a decentralized way without distributing raw data but, nonetheless, the gradients are vulnerable to privacy attacks that include gradient inversion, reconstruction, and membership inference. Differential Privacy (DP) is broadly used to address these risks...
This study addresses the challenges of privacy leakage and data silos in multi-source heterogeneous data interaction within smart grids by designing a federated multi-source data fusion architecture that combines adaptive local differential privacy with feature space alignment. This architecture utilizes Hessian matrix...
FedE, a multi-precision, multi-source, heterogeneous privacy-preserving federated learning training method based on functional encryption that enhances numerical adaptation during ciphertext computation and prevents model parameter updates from easily compromising privacy in cross-institutional federated learning.
Federated learning (FL) enables collaborative model training across multiple clients in a privacy-preserving manner. However, the employment of homomorphic encryption algorithms might lead to high computational cost while the application of differential privacy (DP) methods would sacrifice model performance. To establi...
Federated learning (FL) enables multiple devices to collaboratively train machine learning models without sharing raw data, making it well-suited for Internet of Things (IoT) applications. However, this approach is not fully secure, as the exchanged gradients can still leak sensitive information. Attacks such as Deep L...
FedDyna is proposed, a novel framework that uses an adaptive noise injection mechanism to enhance privacy in non-IID data environments and effectively demonstrates the significant divergence between dynamic and static noise under non-IID settings.
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