IFed: A novel federated learning framework for local differential privacy in Power Internet of Things

Abstract

Nowadays, wireless sensor network technology is being increasingly popular which is applied to a wide range of Internet of Things. Especially, Power Internet of Things is an important and rapidly growing section in Internet of Thing systems, which benefited from the application of wireless sensor networks to achieve fine-grained information collection. Meanwhile, the privacy risk is gradually exposed, which is the widespread concern for electricity power consumers. Non-intrusive load monitoring, in particular, is a technique to recover state of appliances from only the energy consumption data, which enables adversary inferring the behavior privacy of residents. There can be no doubt that applying local differential privacy to achieve privacy preserving in the local setting is more trustworthy than centralized approach for electricity customers. Although it is hard to control the risk and achieve the trade-off between privacy and utility by traditional local differential privacy obfuscation mechanisms, some existing obfuscation mechanisms based on artificial intelligence, called advanced obfuscation mechanisms, can achieve it. However, the large computing resource consumption to train the machine learning model is not affordable for most Power Internet of Thing terminal. In this article, to solve this problem, IFed was proposed—a novel federated learning framework that let electric provider who normally is adequate in computing resources to help Power Internet of Thing users. First, the optimized framework was proposed in which the trade-off between local differential privacy, data utility, and resource consumption was incorporated. Concurrently, the following problem of privacy preserving on the machine learning model transport between electricity provider and customers was noted and resolved. Last, users were categorized based on different levels of privacy requirements, and stronger privacy guarantee was provided for sensitive users. The formal local differential privacy analysis and the experiments demonstrated that IFed can fulfill the privacy requirements for Power Internet of Thing users.

Federated Learning for Internet of Things: A Comprehensive Survey

“…Moreover, by cooperating data centers over different urban areas, the prediction model can achieve a high learning performance with better accuracy rate, compared to centralized learning solutions at a single server [158]. Another FL algorithm is also designed in [159] for electricity power learning in power IoT networks consisting of electric providers and IoT users. A communication model is then formulated based on an FL process that aims to solve the tradeoff between resource consumption, user utility and local differential privacy.…”

Current Research Trends in IoT Security: A Systematic Mapping Study

“…In addition, intelligent IoT applications enhanced with cloud, edge, and fog computing increasingly deal with personal information to provide intelligent services, and many studies on personal information protection and data protection are being conducted [80][81][82][83]. Among the personal information protection approaches, differential privacy is gaining attention as a mechanism to provide intelligent services by grasping user behavior patterns without infringing on personal information by adding noise to prevent the identification of personal information [81,[84][85][86][87][88].…”

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

“…Al-Saffar and Musilek [84,85] used deep reinforcement learning to solve the optimal power flow (OPF) problem while considering the microgrids as agents. Federated learning has also been used for increasing cybersecurity [86] and customer privacy [87] in non-intrusive load monitoring (NILM). A summary of the discussed distributed learning applications in power systems is given in Table 2.…”