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A Secure Federated Learning Framework for Residential Short Term Load Forecasting
journal contributionposted on 2023-07-28, 05:33 authored by MA Husnoo, Adnan AnwarAdnan Anwar, Nasser Hosseinzadeh, Shama IslamShama Islam, AN Mahmood, Robin Ram Mohan DossRobin Ram Mohan Doss
Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers’ privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preserving machine learning alternative which enables collaborative learning of a model without exposing private raw data for short term load forecasting. Despite its virtue, standard FL is still vulnerable to an intractable cyber threat known as Byzantine attack carried out by faulty and/or malicious clients. Therefore, to improve the robustness of federated short-term load forecasting against Byzantine threats, we develop a state-of-the-art differentially private secured FL-based framework that ensures the privacy of the individual smart meter’s data while protect the security of FL models and architecture. Our proposed framework leverages the idea of gradient quantization through the Sign Stochastic Gradient Descent (SignSGD) algorithm, where the clients only transmit the ‘sign’ of the gradient to the control centre after local model training. As we highlight through our experiments involving benchmark neural networks with a set of Byzantine attack models, our proposed approach mitigates such threats quite effectively and thus outperforms conventional FedSGD models.
JournalIEEE Transactions on Smart Grid
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
46 Information and Computing Sciences4611 Machine Learning4604 Cybersecurity and Privacy4008 Electrical engineering4009 Electronics, sensors and digital hardware4606 Distributed computing and systems softwareInterdisciplinary Engineering not elsewhere classifiedElectrical and Electronic Engineering not elsewhere classified