Differentially Private Stochastic Gradient Descent (DP-SGD) is a widely adopted technique for privacy-preserving deep learning. A critical challenge in DP-SGD is selecting the optimal clipping threshold C, which involves balancing the trade-off between clipping bias and noise magnitude, incurring substantial privacy and computing overhead during hyperparameter tuning. In this paper, we propose Dynamic Clipping DP-SGD (DC-SGD), a framework that leverages differentially private histograms to estimate gradient norm distributions and dynamically adjust the clipping thresholdC. Our framework includes two novel mechanisms: DC-SGD-P and DC-SGD-E. DC-SGD-P adjusts the clipping threshold based on a percentile of gradient norms, while DC-SGD-E minimizes the expected squared error of gradients to optimizeC. These dynamic adjustments significantly reduce the burden of hyperparameter tuningC. The extensive experiments on various deep learning tasks, including image classification and natural language processing, show that our proposed dynamic algorithms achieve up to 9 times acceleration on hyperparameter tuning than DP-SGD. And DC-SGD-E can achieve an accuracy improvement of 10.62% on CIFAR10 than DP-SGD under the same privacy budget of hyperparameter tuning. We conduct rigorous theoretical privacy and convergence analyses, showing that our methods seamlessly integrate with the Adam optimizer. Our results highlight the robust performance and efficiency of DC-SGD, offering a practical solution for differentially private deep learning with reduced computational overhead and enhanced privacy guarantees.

In the modern smart home, smart meters, and Internet of Things (IoT) have been massively deployed to replace traditional analogue meters. It digitalises the data collection and the meter readings. The data can be wirelessly transmitted that significantly reduces manual works. However, the community of smart home network is vulnerable to energy theft. Such attacks cannot be effectively detected since the existing techniques require certain devices to be installed to work. This imposes a challenge for energy theft detection systems to be implemented despite the lack of energy monitoring devices. This paper develops an energy detection system called smart energy theft system (SETS) based on machine learning and statistical models. There are three stages of decision-making modules, the first stage is the prediction model which uses multimodel forecasting system. This system integrates various machine learning models into a single forecast system for predicting the power consumption. The second stage is the primary decision making model that uses simple moving average (SMA) for filtering abnormally. The third stage is the secondary decision making model that makes the final stage of the decision on energy theft. The simulation results demonstrate that the proposed system can successfully detect 99.96% accuracy that enhances the security of the IoT-based smart home.

Internet of Things makes deployment of smart home concept easy and real. Smart home concept ensures residents to control, monitor, and manage their energy consumption without any wastage. This paper presents a self-learning home management system. In the proposed system, a home energy management system, demand side management system, and supply side management system were developed and integrated for real time operation of a smart home. This integrated system has some capabilities such as price forecasting, price clustering, and power alert system to enhance its functions. These enhancing capabilities were developed and implemented using computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data was collected from a Singapore smart home and a realistic experimental case study was carried out. The case study has shown that the developed system has performed well and created energy awareness to the residents. This proposed system also displays its ability to customize the model for different types of environments compared to traditional smart home models.

Smart Home is a modern home that allows residents to have high-level comfort with effective use of electricity. These objectives can be achieved by applying suitable and promising optimization algorithms and techniques. This paper presents a demand side management strategy which was integrated into the existing Home Energy Management System (HEMS). Home energy management system is a Multi-Agent System (MAS) based decentralized architecture proposed by the authors. This intelligent energy management system was developed on an IEEE FIPA (Foundation for Intelligent Physical Agents) compliant multi-agent platform. This enables agents to communicate, interact and negotiate with energy sources and devices of the smart home to provide the most efficient energy usage and minimize the cost of electricity bills. This also results some peak load shaving of the power distribution system of the smart home. Simulation studies show the potential of proposed multi-agent system technique together with the demand side management strategy to provide the optimum solution for smart home energy management.