4 research outputs found
A Secure Federated Learning Framework for Residential Short Term Load Forecasting
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 Fed-SGD models
Do not get fooled: Defense against the one-pixel attack to protect IoT-enabled Deep Learning systems
Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey
The rapid evolution of the Internet of Things (IoT) paradigm during the last decade has lead to its adoption in critical infrastructure. However, the multitude of benefits that are derived from the IoT paradigm are short-lived due to the exponential rise in the associated security and privacy threats. Adversaries carry out privacy-oriented attacks to gain access to the sensitive and confidential data of critical infrastructure for various self-centered, political and commercial gains. In the past, researchers have employed several privacy preservation approaches including cryptographic encryption and k-anonymity to secure IoT-enabled critical infrastructure. However, for various reasons, those proposed solutions are not well suited for modern IoT-enabled critical infrastructure. Therefore, Dwork’s differential privacy has emerged as the most viable privacy preservation strategy for IoT-enabled critical infrastructure. This paper provides a comprehensive and extensive survey of the application and implementation of differential privacy in four major application domains of IoT-enabled critical infrastructure: Smart Grids (SGs), Intelligent Transport Systems (ITSs), healthcare and medical systems, and Industrial Internet of Things (IIoT). Finally, we discuss some promising future research directions in differential privacy for IoT-enabled critical infrastructure
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers
As Smart Meters are collecting and transmitting household energy consumption
data to Retail Energy Providers (REP), the main challenge is to ensure the
effective use of fine-grained consumer data while ensuring data privacy. In
this manuscript, we tackle this challenge for energy load consumption
forecasting in regards to REPs which is essential to energy demand management,
load switching and infrastructure development. Specifically, we note that
existing energy load forecasting is centralized, which are not scalable and
most importantly, vulnerable to data privacy threats. Besides, REPs are
individual market participants and liable to ensure the privacy of their own
customers. To address this issue, we propose a novel horizontal
privacy-preserving federated learning framework for REPs energy load
forecasting, namely FedREP. We consider a federated learning system consisting
of a control centre and multiple retailers by enabling multiple REPs to build a
common, robust machine learning model without sharing data, thus addressing
critical issues such as data privacy, data security and scalability. For
forecasting, we use a state-of-the-art Long Short-Term Memory (LSTM) neural
network due to its ability to learn long term sequences of observations and
promises of higher accuracy with time-series data while solving the vanishing
gradient problem. Finally, we conduct extensive data-driven experiments using a
real energy consumption dataset. Experimental results demonstrate that our
proposed federated learning framework can achieve sufficient performance in
terms of MSE ranging between 0.3 to 0.4 and is relatively similar to that of a
centralized approach while preserving privacy and improving scalability