A Privacy-Preserving Deep Learning Scheme for Edge-enhanced Smart Homes

Dong, Yueyu; Dai, Fei; Qin, Mingming

doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9928002

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The HCPS-related research listed above mainly use or can use AI related technology [67,74,81,83,101], which can be seen as the nervous system of HCPS, especially in EI; therefore, its performance should be paced with HCPS's demand. Through using AI within HCPS, the operating efficiency and optimal management ability can be largely improved, which can potentially map humans' abilities into the machine operation in EI system, so that complex handling work, such as troubleshooting, can be automatically solved.…”

Section: Aimentioning

confidence: 99%

Human-Cyber-Physical Systems for Energy Internet—A Review

et al. 2023

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As the research of HCPS in EI is ongoing, an increasing number of problems should be addressed, such as the leading role or aid function definition of humans, the coordination of humans with cyber and physical equipment, the state awareness of information handling, etc. Therefore, an increasing number of advanced techniques should be considered and adopted, such as IOT, AI and big data, etc., the main target of which is to improve the timely, digitalized, informationized and intelligent characters of the related tasks. This paper first introduces the applications of CPS in every industry, especially in the power grid and EI (Energy Internet), the introduction of which can be classified as a framework design, support technologies and algorithms, system function, power ancillary service, Energy Internet realization, etc. Then, this paper summarizes the related technologies of Human-Cyber-Physical Systems, which mainly include the areas of system design, theoretical analysis, technique development and application forecasting, which emphasizes its applications in EI. For the development of HCPS, it uses the human-in-the-loop concept and digital transformation as the future research and development directions, the performance of which can be largely promoted through these means. With the aid of HCPS, the optimal system performance of EI can be achieved more robustly, which finally evolves into an integrated and comprehensive modern power grid.

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Section: Aimentioning

confidence: 99%

Human-Cyber-Physical Systems for Energy Internet—A Review

et al. 2023

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Federated Learning: Navigating the Landscape of Collaborative Intelligence

Lazaros,

Koumadorakis,

Vrahatis

et al. 2024

Electronics

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As data become increasingly abundant and diverse, their potential to fuel machine learning models is increasingly vast. However, traditional centralized learning approaches, which require aggregating data into a single location, face significant challenges. Privacy concerns, stringent data protection regulations like GDPR, and the high cost of data transmission hinder the feasibility of centralizing sensitive data from disparate sources such as hospitals, financial institutions, and personal devices. Federated Learning addresses these issues by enabling collaborative model training without requiring raw data to leave its origin. This decentralized approach ensures data privacy, reduces transmission costs, and allows organizations to harness the collective intelligence of distributed data while maintaining compliance with ethical and legal standards. This review delves into FL’s current applications and its potential to reshape IoT systems into more collaborative, privacy-centric, and flexible frameworks, aiming to enlighten and motivate those navigating the confluence of machine learning and IoT advancements.

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A Privacy-Preserving Deep Learning Scheme for Edge-enhanced Smart Homes

Cited by 2 publications

References 11 publications

Human-Cyber-Physical Systems for Energy Internet—A Review

Human-Cyber-Physical Systems for Energy Internet—A Review

Federated Learning: Navigating the Landscape of Collaborative Intelligence

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