FedLearnSP: Preserving Privacy and Security Using Federated Learning and Edge Computing

Makkar, Aaisha; Ghosh, Uttam; Rawat, Danda B.; Abawajy, Jemal

doi:10.1109/mce.2020.3048926

Cited by 26 publications

(12 citation statements)

References 5 publications

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“…e risks related to the university laboratory system and environment involved in the daily production process need to be analyzed and evaluated beforehand in order to implement safety control measures and safety management measures in the course of events and to provide the most basic original data comparison for the summary review and rectification afterward, so as to finally realize the safety and stability of university laboratories. Systematization is a way of thinking that emphasizes a holistic view and a global view [2]. Systematization means that in the process of daily safety of university laboratories, we take the goal we want to achieve, the factors to be considered to achieve the goal, the optimization of the implementation process, and the consideration of the possible impact on the future, etc.…”

Section: Eoretical Research Directionmentioning

confidence: 99%

Construction of a Safety Management System for University Laboratories Based on Artificial Intelligence and IoT Technology

Ding

2022

International Transactions on Electrical Energy Systems

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Based on IoT technology, it can combine the actual needs of the university laboratory system to establish an equipment safety monitoring system, share data in real time, and monitor the operation equipment of university laboratories comprehensively. Through the collection of equipment-related data, we have a comprehensive analysis of data in order to have an accurate judgment of the state of the equipment, so that the chances of equipment failure can be gradually reduced. In this paper from the architectural point of view, IoT contains four layers: perception layer, network layer, platform layer, and application layer. The perception layer mainly solves the problem of data collection, the network layer mainly solves the problem of data transmission, the platform layer mainly solves the problem of data management, and the application layer mainly solves the problem of data value creation. From the technical point of view, the university laboratories’ Internet of Things widely used big data, cloud computing, Internet of Things, artificial intelligence, blockchain, edge computing, and other types of information technology and intelligent technology. From the role, the university laboratories’ IoT is to provide sufficient and effective information and technical support for planning and construction, production and operation, operation and management, integrated services, new business, new model development, enterprise ecosystem construction, and other aspects by bringing together resources from all aspects. The whole university laboratory system sustainability monitoring equipment maintains the continuity of monitoring and can be the first time to staff feedback fault information, to achieve the human-machine dialogue function; staff in the control of equipment safety monitoring system just enter the relevant instructions to fully achieve the purpose of control; staff can also print a certain period of time fault-related information, which will be properly stored in a reasonable location.

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Section: Eoretical Research Directionmentioning

confidence: 99%

Construction of a Safety Management System for University Laboratories Based on Artificial Intelligence and IoT Technology

Ding

2022

International Transactions on Electrical Energy Systems

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“…To solve these privacy concerns, we introduced an FL framework and developed a privacy-protected collaborative classification model for Android malware. An FL framework allows Android clients to keep the malware dataset locally and collaboratively learn classification models, which means that any third party cannot access the user’s raw data [ 28 ]. The framework of Federated learning consists of a server and multiple clients [ 29 ].…”

Section: System Model and Problem Descriptionmentioning

confidence: 99%

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

Jiang

Yin

Xia

et al. 2022

Entropy

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With the popularity of Android and its open source, the Android platform has become an attractive target for hackers, and the detection and classification of malware has become a research hotspot. Existing malware classification methods rely on complex manual operation or large-volume high-quality training data. However, malware data collected by security providers contains user privacy information, such as user identity and behavior habit information. The increasing concern for user privacy poses a challenge to the current malware classification scheme. Based on this problem, we propose a new android malware classification scheme based on Federated learning, named FedHGCDroid, which classifies malware on Android clients in a privacy-protected manner. Firstly, we use a convolutional neural network and graph neural network to design a novel multi-dimensional malware classification model HGCDroid, which can effectively extract malicious behavior features to classify the malware accurately. Secondly, we introduce an FL framework to enable distributed Android clients to collaboratively train a comprehensive Android malware classification model in a privacy-preserving way. Finally, to adapt to the non-IID distribution of malware on Android clients, we propose a contribution degree-based adaptive classifier training mechanism FedAdapt to improve the adaptability of the malware classifier based on Federated learning. Comprehensive experimental studies on the Androzoo dataset (under different non-IID data settings) show that the FedHGCDroid achieves more adaptability and higher accuracy than the other state-of-the-art methods.

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“…The notion behind the FL paradigm is to enable different data owners (participants) to collaboratively train a joint ML/DL without revealing their locally stored private data, hence relieving their privacy worries [4]. Generally, the FL framework consists of two main entities namely the participants and the FL server.…”

Section: Definition Of Federated Learningmentioning

confidence: 99%

“…At this point, we present two techniques that could realize efficient privacy assurances by incorporating noise into the shared network parameters. First, the Gaussian technique include the noise N 0, sd 2 h σ 2 with average 0 and standard deviation sd h σ to the operation h(D) that has a universal sympathy s h .h(D) that will follow the DP with ( , δ) when the δ ≥ 4 5 exp −(σ ) 2 /2 and the < 1. Hence, we associated the parameter of Gaussian noise σ to both privacy parameters σ andδ.…”

Section: Differential Privacymentioning

confidence: 99%

Federated Learning for Privacy-Preserving Internet of Things

Abdel‐Basset

Moustafa

Hawash

et al. 2021

Deep Learning Techniques for IoT Security and Privacy

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The rapid evolution of the Internet of Things (IoT) and relevant applications have been paving the way toward the fulfillment of smart cities. Smart cities are thought to come up with multiple crucial smart IoT applications i.e., smart manufacturing, smart transportation, autonomous driving/flight, smart buildings, smart healthcare, smart grid, and so many others [1]. Effective deployment of these IoT applications and services and making them available for users usually entail a vast number of IoT devices. Conferring to some global statistics, the total of IoT devices is believed to rises to 125 billion by 2030. This in turn reflects expected large growth in the amount of IoT-generated data. some other statistics show that, by 2025, the size of IoT-generated data will grow up to reach 79.4 zettabytes (ZB). That incredible growth in the size of IoT networks and associated data volume afford fascinating opportunities for deep learning in IoT environments. To this end, one can use centralized deep learning frameworks (discussed in Chaps. 4-7) to support the design of smart IoT applications. Nevertheless, centralized deep learning solutions struggle with the intrinsic problem of leakage of privacy of users owing to the requirement to transmit the local data at IoT devices data to a central third-party server (i.e., cloud server or datacenter) for training purposes. Additionally, centralized deep learning could not be viable in the case of big volumes of data (e.g., astronomical data) and placed at various geographic locations.To tackle the above-mentioned challenge of big and geographically dispersed data, distributed deep learning solution emerged to disseminate learning workload across manifold computing agents or devices. Those manifold devices could later train many models at distributed locations simultaneously to construct a cohesive deep learning model [2]. Distributed learning empowers more efficient computing through concurrent training of deep learning architecture. Two types of learning approaches, namely data-parallel and model-parallel approaches, could be exploited Electronic Supplementary Material The online version of this chapter

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FedLearnSP: Preserving Privacy and Security Using Federated Learning and Edge Computing

Cited by 26 publications

References 5 publications

Construction of a Safety Management System for University Laboratories Based on Artificial Intelligence and IoT Technology

Construction of a Safety Management System for University Laboratories Based on Artificial Intelligence and IoT Technology

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

Federated Learning for Privacy-Preserving Internet of Things

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