Federated Machine Learning in Vehicular Networks: A summary of Recent Applications

Tan, Kang; Bremner, Duncan; Kernec, Julien Le; Imran, Muhammad Ali

doi:10.1109/ucet51115.2020.9205482

Cited by 31 publications

(13 citation statements)

References 20 publications

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“…FL assisted blockchain is proposed in [31] to adjust block arrival rate in order to reduce communication latency and consensus delay among vehicles. Applications of FL in vehicular networks are summarized in [32] and most of the recent applications focus on resource management, performance optimization in computing tasks and user-end services. However, FL can also be promising in message delivery and relay node selection.…”

Section: B Fl In Vehicular Networkmentioning

confidence: 99%

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Ayaz

Sheng

Tian

et al. 2022

IEEE Trans. Veh. Technol.

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Section: B Fl In Vehicular Networkmentioning

confidence: 99%

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Ayaz

Sheng

Tian

et al. 2022

IEEE Trans. Veh. Technol.

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“…Araç ağlarında sensörlerin artmasıyla, özellikle de otonom araçların yaygınlaşması ile bu alanda daha fazla veri kullanabilmek ve Makine Öğrenimi modellerini eğitmek mümkün hale gelmiştir. Makine Öğrenimi tabanlı modeller genellikle hem araç yönetimine hem de trafik yönetimine uygulanır [24]. Mevcut otonom sürüş modelleri eğitimin yapıldığı konumun dinamik doğası ile sınırlıdır.…”

Section: Nakliyeunclassified

Collaborative Artifical Intelligence Concept: Federated Learning Review

Nergiz

2022

DÜMF MD

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Artificial intelligence (AI) draws its power from big data. However, accessing and processing big data may not always be possible due to both confidentiality and hardware requirements for high computational performance. Federated learning (FL) is a new concept proposed to solve the aforementioned privacy & big data dilemma. FL is also a framework that performs updating of the parameters of a common AI model trained by the different participants and then combining the updated parameters through the coordinator while protecting data privacy. Due to the modular design of the FL concept, the workload is shared among the participants while protecting data privacy. It also provides advantages like scalability in terms of collaborator count and higher performance and lower execution time for some sort of problems. Depending on the similarity of the feature and sample spaces of the collaborators, there are some FL approaches such as horizontal, vertical and transfer. FL is applicable to any field in which machine learning methods are utilized and the data privacy is an important issue. Healthcare services, transportation sector, financial technologies and natural language processing are the prominent fields where horizontal FL concept is applied. On the other hand, AI-based collaborations between the sectors can be developed with vertical and transfer FL concepts.

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“…As its distinctive feature, FL allows the development of a robust and reliable ML model across different domain applications without requiring the raw data to be shared with a central server. It can for example be potentially used in healthcare [31,59,100], Natural Language Processing (NLP) [101], transportation [102,103], and finance [104]. Processing and securing the millions of petabytes of data produced by ubiquitous networked IoT devices have become a challenge with the technological advances and growing popularity of IoT and distributed applications.…”

Section: Fl and Its Applications With Other Technologies In The Conte...mentioning

confidence: 99%

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Rauniyar¹,

Hagos²,

Jha³

et al. 2022

Preprint

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With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. Hence, in recent years, data-driven medical applications have attracted a great deal of attention from academia to industry. This has undoubtedly improved the quality of healthcare delivery. However, AI-based medical applications still have poor adoption due to their difficulties in satisfying strict security, privacy, and quality of service standards such as low latency. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. It also review and discuss the current research trends and their outcomes for designing reliable and scalable FL models. We outline the general FL's statistical problems, device challenges, security, privacy concerns, and its potential in the medical domain. Moreover, our study is also focused on medical applications where we highlight the burden of global cancer and the efficient use of FL for the development of computer-aided diagnosis tools for addressing them. Additionally, the recent literature revealed that FL models ensure a high level of robustness and generalization compared to the traditional data-driven medical applications. We hope that this review serves as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.

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Federated Machine Learning in Vehicular Networks: A summary of Recent Applications

Cited by 31 publications

References 20 publications

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Collaborative Artifical Intelligence Concept: Federated Learning Review

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

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