Federated Learning Role in Big Data, Jot Services and Applications Security, Privacy and Trust in Jot A Survey

Yarradoddi, Supriya; Gadekallu, Thippa Reddy

doi:10.1201/9781003194538-2

Cited by 11 publications

(5 citation statements)

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“…Autonomous driving technology is making its way into regular vehicles with the advancement of vehicular IoT. A reliable self-driving system requires a real-time connection with a multi-access communication environment ( Yarradoddi & Gadekallu, 2022 ). Spatial and temporal variations of the vehicular environment necessitate an intelligent solution that can adapt to the changing climate.…”

Section: Resultsmentioning

confidence: 99%

Integration of federated learning with IoT for smart cities applications, challenges, and solutions

Ghadi,

Mazhar,

Shah

et al. 2023

PeerJ Computer Science

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In the past few years, privacy concerns have grown, making the financial models of businesses more vulnerable to attack. In many cases, it is hard to emphasize the importance of monitoring things in real-time with data from Internet of Things (IoT) devices. The people who make the IoT devices and those who use them face big problems when they try to use Artificial Intelligence (AI) techniques in real-world applications, where data must be collected and processed at a central location. Federated learning (FL) has made a decentralized, cooperative AI system that can be used by many IoT apps that use AI. It is possible because it can train AI on IoT devices that are spread out and do not need to share data. FL allows local models to be trained on local data and share their knowledge to improve a global model. Also, shared learning allows models from all over the world to be trained using data from all over the world. This article looks at the IoT in all of its forms, including “smart” businesses, “smart” cities, “smart” transportation, and “smart” healthcare. This study looks at the safety problems that the federated learning with IoT (FL-IoT) area has brought to market. This research is needed to explore because federated learning is a new technique, and a small amount of work is done on challenges faced during integration with IoT. This research also helps in the real world in such applications where encrypted data must be sent from one place to another. Researchers and graduate students are the audience of our article.

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Section: Resultsmentioning

confidence: 99%

Integration of federated learning with IoT for smart cities applications, challenges, and solutions

Ghadi,

Mazhar,

Shah

et al. 2023

PeerJ Computer Science

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“…In general, big data is currently characterized by seven Vs as follows: volume (a large amount of data), variety (including structured, semi-structured, and unstructured data with different formats), velocity (high rates of data inflow and real-time processing), veracity (detailed data accumulation), value (in-depth and meaningful information), variability (offering extensionality and scalability), and valence (data interconnection) [49]. About the big data workflow, a four-step process should be followed with big data generation (generated from different sources: internal data from hospitals and medical centers, IoT data, data from Internet, and biomedical data), big data acquisition (including data collection, transmission, and pre-processing), big data storage, and big data analysis [50], [51], [52].…”

Section: B Big Datamentioning

confidence: 99%

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

et al. 2023

Self Cite

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The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.INDEX TERMS Metaverse, healthcare, virtual reality, digital twin, cybersecurity.

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“…For the idealized distributed settings with i.i.d. data and no attacks, which cannot be guaranteed for edge IoT systems [56], PSO and FL are integrated [55] in a simplistic manner. The majority of earlier works emphasized client communication and broad optimization to boost the performance of FL.…”

Section: Related Workmentioning

confidence: 99%

Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires

Gadekallu

2023

Sustainability

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Forests are a vital part of the ecological system. Forest fires are a serious issue that may cause significant loss of life and infrastructure. Forest fires may occur due to human or man-made climate effects. Numerous artificial intelligence-based strategies such as machine learning (ML) and deep learning (DL) have helped researchers to predict forest fires. However, ML and DL strategies pose some challenges such as large multidimensional data, communication lags, transmission latency, lack of processing power, and privacy concerns. Federated Learning (FL) is a recent development in ML that enables the collection and process of multidimensional, large volumes of data efficiently, which has the potential to solve the aforementioned challenges. FL can also help in identifying the trends based on the geographical locations that can help the authorities to respond faster to forest fires. However, FL algorithms send and receive large amounts of weights of the client-side trained models, and also it induces significant communication overhead. To overcome this issue, in this paper, we propose a unified framework based on FL with a particle swarm-optimization algorithm (PSO) that enables the authorities to respond faster to forest fires. The proposed PSO-enabled FL framework is evaluated by using multidimensional forest fire image data from Kaggle. In comparison to the state-of-the-art federated average model, the proposed model performed better in situations of data imbalance, incurred lower communication costs, and thus proved to be more network efficient. The results of the proposed framework have been validated and 94.47% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of early warning systems for forest fires.

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Federated Learning Role in Big Data, Jot Services and Applications Security, Privacy and Trust in Jot A Survey

Cited by 11 publications

References 0 publications

Integration of federated learning with IoT for smart cities applications, challenges, and solutions

Integration of federated learning with IoT for smart cities applications, challenges, and solutions

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires

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