Privacy preserving Federated Learning framework for IoMT based big data analysis using edge computing

Nair, Akarsh K.; Sahoo, Jayakrushna; Raj, Ebin Deni

doi:10.1016/j.csi.2023.103720

Cited by 56 publications

(15 citation statements)

References 54 publications

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“…The authors show that this method works well with big data sets and that great accuracy may be attained while maintaining anonymity. Nair et al 21 devise and execute a privacy‐focused FL approach for analyzing substantial IoT‐generated data, employing edge computing. Their evaluation, performed on an authentic healthcare data set, demonstrates the framework's ability to maintain high accuracy levels while safeguarding data privacy.…”

Section: Related Workmentioning

confidence: 99%

Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing

Ibrahim Khalaf,

S.R,

Algburi

et al. 2024

Security and Privacy

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The federated learning has gained prominent attention as a collaborative machine learning method, allowing multiple users to jointly train a shared model without directly exchanging raw data. This research addresses the fundamental challenge of balancing data privacy and utility in distributed learning by introducing an innovative hybrid methodology fusing differential privacy with federated learning (HDP‐FL). Through meticulous experimentation on EMNIST and CIFAR‐10 data sets, this hybrid approach yields substantial advancements, showcasing a noteworthy 4.22% and up to 9.39% enhancement in model accuracy for EMNIST and CIFAR‐10, respectively, compared to conventional federated learning methods. Our adjustments to parameters highlighted how noise impacts privacy, showcasing the effectiveness of our hybrid DP approach in striking a balance between privacy and accuracy. Assessments across diverse FL techniques and client counts emphasized this trade‐off, particularly in non‐IID data settings, where our hybrid method effectively countered accuracy declines. Comparative analyses against standard machine learning and state‐of‐the‐art FL approaches consistently showcased the superiority of our proposed model, achieving impressive accuracies of 96.29% for EMNIST and 82.88% for CIFAR‐10. These insights offer a strategic approach to securely collaborate and share knowledge among IoT devices without compromising data privacy, ensuring efficient and reliable learning mechanisms across decentralized networks.

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Section: Related Workmentioning

confidence: 99%

Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing

Ibrahim Khalaf,

S.R,

Algburi

et al. 2024

Security and Privacy

View full text Add to dashboard Cite

show abstract

“…There are numerous applications specific to IoT in healthcare and smart cities, where they are largely utilized for supervising surveillance networks, monitory systems, smart healthcare systems, transport networks, and smart homes [5,6,7,8,9]. This has resulted in deployment to a huge amount of sensor nodes; and accelerated the implementation concept of smart solutions [2,10,11,12]. Sensors are employed to monitor and maintain different functionalities.…”

Section: Contextmentioning

confidence: 99%

“…The adoption of smart cities and smart healthcare concepts employing big data servers in their networking architecture has created vulnerabilities to their secure functioning. So, enhancing the robustness of such networks has gained much focus in the latest research works [11,12,17,18].…”

Section: Topology Robustness and Its Importancementioning

confidence: 99%

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Topology Robustness Modelling against Targeted Attacks for Smart Healthcare System using Go-GA

Changazi

Bakhshi

Yousaf

et al. 2023

Preprint

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The Internet of Things (IoT) has become a pivotal component of modern technology, enabling the interconnection of numerous intelligent devices. It is envisioned to revolutionize various fields through the realization of numerous applications across industries, including healthcare, precision farming, transportation, and smart cities. However, the practical problem of node and link failures due to various constraints, such as low power backup, adverse environmental conditions, fragile metallurgy, and targeted attacks, can negatively impact the overall functionality of IoT networks. To address these challenges, soft and intelligent computing techniques, such as classical heuristic and genetic algorithms, can be utilized to improve the topology robustness of future IoT networks. In this study, we present a system model for a smart healthcare network and evaluate the effectiveness of optimization techniques, including the newly proposed Optimization of Topology for Efficient Convergence (OTEC), which combines a geometric approach in a genetic algorithm with two mechanisms, Efficient Edge Swap (EES) and Node Removal based on Threshold (NRT), to address key limitations in existing techniques. The metric of Schneider R is used to assess the robustness of the topology, with geographic information about IoT nodes and their neighbors stored on a central big data server. The OTEC approach utilizes a nested approach, demonstrating significant improvement over classical genetic algorithms by achieving a 21% increase in Schneider R. Additionally, OTEC effectively addresses limitations present in traditional heuristic algorithms such as ROSE, Simulated Annealing and Hill Climbing. The concept of trustworthiness is also considered from an overall perspective of system functionality. The optimized topology not only improves the robustness of the network but also enhances its trustworthiness, ensuring that the system can operate reliably and ethically. By leveraging soft and intelligent computing techniques, this study provides a valuable contribution to the development of future IoT networks that can handle the challenges of node and link failures while remaining trustworthy and dependable.

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“…Due to the emergence of products such as cloud computing and storage, the data produced by the communication infrastructure are used to generate even more data that are also shared across other entities [73]. The data that are generated from interactions with servers through the IoT can be used to provide useful insights on how telecommunication businesses can manage their processes [127]. In terms of telecommunication business solutions, uploading data to the cloud platform for analysis can be an intensive process, and a lot of insights can be found at the edge.…”

Section: Big Data and Big Data Analytics-the Final Frontiermentioning

confidence: 99%

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

Hlophe,

Maharaj

2023

Applied Sciences

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As a result of the new telecommunication ecosystem landscape, wireless communication has become an interdisciplinary field whose future is shaped by several interacting dimensions. These interacting dimensions, which form the cyber–physical convergence, closely link the technological perspective to its social, economic, and cognitive sciences counterparts. Beyond the current operational framework of the Internet of Things (IoT), network devices will be equipped with capabilities for learning, thinking, and understanding so that they can autonomously make decisions and take appropriate actions. Through this autonomous operation, wireless networking will be ushered into a paradigm that is primarily inspired by the efficient and effective use of (i) AI strategies, (ii) big data analytics, as well as (iii) cognition. This is the Cognitive Internet of People Processes Data and Things (CIoPPD&T), which can be defined in terms of the cyber–physical convergence. In this article, through the discussion of how the cyber–physical convergence and the interacting dynamics of the socio-technical ecosystem are enablers of digital twins (DTs), the network DT (NDT) is discussed in the context of 6G networks. Then, the design and realization of edge computing-based NDTs are discussed, which culminate with the vehicle-to-edge (V2E) use cases.

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Privacy preserving Federated Learning framework for IoMT based big data analysis using edge computing

Cited by 56 publications

References 54 publications

Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing

Federated learning with hybrid differential privacy for secure and reliable cross‐IoT platform knowledge sharing

Topology Robustness Modelling against Targeted Attacks for Smart Healthcare System using Go-GA

From Cyber–Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs

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