Work

Atlas: Ensuring Accuracy for Privacy-Preserving Federated IoT Applications

Year: 2025

Type: conference-paper

Abstract: In smart Internet of Things (IoT) applications, edge devices often collect and store limited data, which is insufficient for training modern deep learning models. Collaborative training methods like cloud computing and federated learning enable robust models for IoT applications, yet introduce data privacy concerns due to central data collection and model inversion attacks. Remedies such as differential privacy can bring data privacy protection but dramatically degrade the accuracy performance of IoT applications. To safeguard user data privacy while maintaining application quality, it is imperative to establish a framework capable of preserving user privacy without compromising accuracy standards.

Source: ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS)

DOI: 10.1145/3716550.3722017

Authors: Jiechao Gao, Mingyue Tang, Wenpeng Wang, Tushar Routh, Bradford Campbell

Institutions: University of Virginia, University of Illinois Urbana-Champaign

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Universitas Brawijaya

Awards: Collaborative Research: PPoSS: LARGE: Co-designing Hardware, Software, and Algorithms to Enable Extreme-Scale Machine Learning Systems, G956039121