A Secure Federated Learning Framework for Residential Short Term Load Forecasting

Author:

Husnoo Muhammad Akbar1ORCID,Anwar Adnan1ORCID,Hosseinzadeh Nasser2ORCID,Islam Shama Naz2ORCID,Mahmood Abdun Naser3ORCID,Doss Robin1ORCID

Affiliation:

1. Centre for Cyber Security Research and Innovation (CSRI), Deakin University, Australia

2. Centre for Smart Power and Energy Research (CSPER), Deakin University, Australia

3. Department of Computer Science and IT, Latrobe University, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-Term Load Forecasting in Electricity Wholesale Markets;IEEE Transactions on Network Science and Engineering;2024-09

2. Energy-Aware Federated Learning for AQI Pollutants Forecasting in Edge Networks;IEEE Transactions on Network Science and Engineering;2024-09

3. PPFGED: Federated learning for graphic element detection with privacy preservation in multi-source substation drawings;Expert Systems with Applications;2024-06

4. Deep Learning-Powered Intrusion Detection Systems: Enhancing Efficiency in Network Security;2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI);2024-05-09

5. Privacy Leakage in Federated Home Applications Using Gradient Inversion Algorithms;2024 IEEE International Conference on Industrial Technology (ICIT);2024-03-25

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