Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges

Pouriyeh, Seyedamin; Shahid, Osama; Parizi, Reza M.; Sheng, Quan Z.; Srivastava, Gautam; Zhao, Liang; Nasajpour, Mohammad

doi:10.3390/app12188980

Cited by 21 publications

(4 citation statements)

References 57 publications

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“…It is a good research tool that can manage the complications coming from simulated environments since it is a fully configurable tool that allows the expansion and formulation of rules in every software stack component. The proposed Averaged One-Dependence Estimators (AODE) and SELECT Applicable Only To Parallel Server (ASA) compare with the Beyond fifth Generation (B5G), Fully Automated Unmanned Aerial Vehicles (FAUAV) [ 2 ], Maximum Correlation Criterion, And Minimum Dependence Criterion (MCCMDC) [ 40 ], Multi Independent Latent Component Naive Bayes Classifier (MILC-NB) [ 3 ] and Correlation-Augmented Naïve Bayes (CAN) [ 28 ] Algorithm. The analyze results are then shown in Table 1 and Figure 4 .…”

Section: Results Comparison Discussion With Data Modulesmentioning

confidence: 99%

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

Thirumalaisamy

Basheer

Shitharth

et al. 2022

Sensors

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There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.

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Section: Results Comparison Discussion With Data Modulesmentioning

confidence: 99%

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

Thirumalaisamy

Basheer

Shitharth

et al. 2022

Sensors

Self Cite

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“…The consensus is that FRSs can provide a secure, efficient, and cost-effective way to share sensitive patient data between organizations. Studies have also shown that FRSs can reduce the cost of healthcare data storage, improve the quality of care, and reduce the burden of compliance with data privacy regulations [158,159]. FRSs can benefit healthcare in a number of ways.…”

Section: Healthcarementioning

confidence: 99%

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

et al. 2023

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Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.

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“…This democratic voting process helps to ensure robust and accurate predictions. Due to their robustness, simplicity, and ability to handle high-dimensional datasets, ETs have found widespread applications in various fields, including computer vision, bioinformatics, and finance [66,67]. The randomization techniques employed by ETs contribute to their effectiveness as a powerful and flexible machine learning algorithm.…”

Section: Extra Treesmentioning

confidence: 99%

Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning

Akbulut,

Cifci,

Aslan

2023

Applied Sciences

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In the face of mounting global challenges stemming from population growth and climate fluctuations, the sustainable management of water resources emerges as a paramount concern. This scientific endeavor casts its gaze upon the Upper Euphrates basin, homing in on the Tunceli Munzur water sub-basin and the Sakarya Basin’s Kütahya Porsuk Stream Beşdeğirmen rivers. The investigation unfolds through the intricate analysis of daily average flow data, total daily precipitation, and daily average air temperature values, with the objective of unraveling the complexities of future water potential estimation. Central to our exploration are a series of well-established techniques including linear regression (LR), support vector regression (SVR), decision tree (DT), random forest (RF), and extra trees regression (ETR). We employ these methodologies diligently to decipher patterns woven within the dataset, fostering an informed understanding of water dynamics. To ascend the pinnacle of estimation accuracy, we introduce a groundbreaking hybrid approach, wherein the enigmatic wavelet transform (WT) technique assumes a pivotal role. Through systematic stratification of our dataset into training, validation, and test sets, comprising roughly 65%, 15%, and 20% of the data, respectively, a comprehensive experiment takes shape. Our results unveil the formidable performance of the ETR method, achieving a striking 88% estimation accuracy for the Porsuk Stream Beşdeğirmen, while the RF method garners a commendable 85.2% success rate for the Munzur water Melekbahçe. The apex of innovation unfolds within our hybrid model, a harmonious fusion of methodologies that transcends their individual capacities. This composite entity elevates estimation success rates by a remarkable 20% for the Munzur water Melekbahçe and an appreciable 11% for the Porsuk Stream Beşdeğirmen. This amalgamation culminates in an extraordinary overall success rate of 97.7%. Our findings transcend mere insights, resonating as guiding beacons for navigating the intricate maze of water resource management in an era marked by uncertainties. This study underscores the indispensability of advanced mathematical paradigms and machine learning frontiers, fortifying the bedrock of sustainable water resource management for the generations to come. By harnessing the fusion of federated learning and a constellation of innovative techniques, we endeavor to illuminate the path towards deciphering the complex tapestry of water resource estimation and management, facilitating a resilient and enduring aquatic world.

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Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges

Cited by 21 publications

References 57 publications

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning

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