Adaptive Privacy-Preserving Machine Learning Framework for Distributed Real-Time Systems

DOI

10.2991/978-94-6463-940-7_6How to use a DOI?

Keywords

Privacy-Preserving Machine Learning; Blockchain; Real-Time Applications; Distributed Systems

Abstract

Distributed Systems play an essential role in enabling real-time decision-making in Internet of Things (IoT) applications. However, maintaining strong data privacy while achieving high performance remains a persistent challenge. Traditional approaches, such as Federated Learning (FL) and Differential Privacy (DP), often struggle to perform effectively in dynamic environments where devices exhibit diverse capabilities and network conditions are inconsistent. This paper introduces the Adaptive Privacy-Preserving Distributed Learning Algorithm (APPDLA), a flexible and secure solution designed to address these issues. APPDLA protects sensitive data using differential privacy and leverages Blockchain technology to provide decentralized, tamper-proof model updates. It dynamically categorizes devices based on their reliability and computational power. This ensures efficient resource utilization and uninterrupted learning, even in changing environments. Our experiments show that APPDLA outperforms traditional approaches by delivering higher accuracy, faster processing, and lower computational costs. This makes it a powerful tool for privacy-focused, real-time applications in fields such as healthcare, smart cities, and finance.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Mahesh Babu
AU  - M. V. S. S. Nagendranath
PY  - 2025
DA  - 2025/12/31
TI  - Adaptive Privacy-Preserving Machine Learning Framework for Distributed Real-Time Systems
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 51
EP  - 64
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_6
DO  - 10.2991/978-94-6463-940-7_6
ID  - Babu2025
ER  -