Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption
Homomorphic Encryption (HE) introduces new dimensions of security and privacy within federated learning (FL) and internet of things (IoT) frameworks that allow preservation of user privacy when handling data for FL occurring in Smart Grid (SG) technologies. In this paper, we propose a novel SG IoT f...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/12/3700 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849704971158683648 |
|---|---|
| author | Filip Jerkovic Nurul I. Sarkar Jahan Ali |
| author_facet | Filip Jerkovic Nurul I. Sarkar Jahan Ali |
| author_sort | Filip Jerkovic |
| collection | DOAJ |
| description | Homomorphic Encryption (HE) introduces new dimensions of security and privacy within federated learning (FL) and internet of things (IoT) frameworks that allow preservation of user privacy when handling data for FL occurring in Smart Grid (SG) technologies. In this paper, we propose a novel SG IoT framework to provide a solution for predicting energy consumption while preserving user privacy in a smart grid system. The proposed framework is based on the integration of FL, edge computing, and HE principles to provide a robust and secure framework to conduct machine learning workloads end-to-end. In the proposed framework, edge devices are connected to each other using P2P networking, and the data exchanged between peers is encrypted using Cheon–Kim–Kim–Song (CKKS) fully HE. The results obtained show that the system can predict energy consumption as well as preserve user privacy in SG scenarios. The findings provide an insight into the SG IoT framework that can help network researchers and engineers contribute further towards developing a next-generation SG IoT system. |
| format | Article |
| id | doaj-art-404d41c2bd8a4d7e89d3490234fbd7f2 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-404d41c2bd8a4d7e89d3490234fbd7f22025-08-20T03:16:35ZengMDPI AGSensors1424-82202025-06-012512370010.3390/s25123700Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic EncryptionFilip Jerkovic0Nurul I. Sarkar1Jahan Ali2Department of Computer and Information Sciences, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandDepartment of Computer and Information Sciences, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandDepartment of Computer and Information Sciences, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandHomomorphic Encryption (HE) introduces new dimensions of security and privacy within federated learning (FL) and internet of things (IoT) frameworks that allow preservation of user privacy when handling data for FL occurring in Smart Grid (SG) technologies. In this paper, we propose a novel SG IoT framework to provide a solution for predicting energy consumption while preserving user privacy in a smart grid system. The proposed framework is based on the integration of FL, edge computing, and HE principles to provide a robust and secure framework to conduct machine learning workloads end-to-end. In the proposed framework, edge devices are connected to each other using P2P networking, and the data exchanged between peers is encrypted using Cheon–Kim–Kim–Song (CKKS) fully HE. The results obtained show that the system can predict energy consumption as well as preserve user privacy in SG scenarios. The findings provide an insight into the SG IoT framework that can help network researchers and engineers contribute further towards developing a next-generation SG IoT system.https://www.mdpi.com/1424-8220/25/12/3700federated learning (FL)internet of things (IoT)smart grid (SG)edge computingmachine learning (ML)internet privacy and security |
| spellingShingle | Filip Jerkovic Nurul I. Sarkar Jahan Ali Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption Sensors federated learning (FL) internet of things (IoT) smart grid (SG) edge computing machine learning (ML) internet privacy and security |
| title | Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption |
| title_full | Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption |
| title_fullStr | Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption |
| title_full_unstemmed | Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption |
| title_short | Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption |
| title_sort | smart grid iot framework for predicting energy consumption using federated learning homomorphic encryption |
| topic | federated learning (FL) internet of things (IoT) smart grid (SG) edge computing machine learning (ML) internet privacy and security |
| url | https://www.mdpi.com/1424-8220/25/12/3700 |
| work_keys_str_mv | AT filipjerkovic smartgridiotframeworkforpredictingenergyconsumptionusingfederatedlearninghomomorphicencryption AT nurulisarkar smartgridiotframeworkforpredictingenergyconsumptionusingfederatedlearninghomomorphicencryption AT jahanali smartgridiotframeworkforpredictingenergyconsumptionusingfederatedlearninghomomorphicencryption |