Federated Learning in Vehicular Networks: Opportunities and Solutions

Posner, Jason; Tseng, Lewis; Aloqaily, Moayad; Jararweh, Yaser

doi:10.1109/mnet.011.2000430

Cited by 180 publications

(64 citation statements)

References 10 publications

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“…Federated learning (FL) [40,41] was proposed to prevent data leakage and build machine learning models based on distributed datasets. The federated learning term was proposed by McMahan et al [42] in 2016: "We term our approach Federated Learning since the learning task is solved by a loose federation of participating devices (which we refer to as clients) which are coordinated by a central server".…”

Section: Federated Learningmentioning

confidence: 99%

A Federated Learning Approach to Frequent Itemset Mining in Cyber-Physical Systems

2021

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Effective vector representation has been proven useful for transaction classification and clustering tasks in Cyber-Physical Systems. Traditional methods use heuristic-based approaches and different pruning strategies to discover the required patterns efficiently. With the extensive and high dimensional availability of transactional data in cyber-physical systems, traditional methods that used frequent itemsets (FIs) as features suffer from dimensionality, sparsity, and privacy issues. In this paper, we first propose a federated learning-based embedding model for the transaction classification task. The model takes transaction data as a set of frequent item-sets. Afterward, the model can learn low dimensional continuous vectors by preserving the frequent item-sets contextual relationship. We perform an in-depth experimental analysis on the number of high dimensional transactional data to verify the developed models with attention-based mechanism and federated learning. From the results, it can be seen that the designed model can help and improve the decision boundary by reducing the global loss function while maintaining both security and privacy.

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Section: Federated Learningmentioning

confidence: 99%

A Federated Learning Approach to Frequent Itemset Mining in Cyber-Physical Systems

2021

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“…[10] Threats models and major attacks in FL. Business [11] • Federated vehicular networks, their high-level architecture and enabling technologies.…”

Section: Reference Main Focus Applicationmentioning

confidence: 99%

“…Then, the authors discussed future research directions towards more robust privacy preservation in FL. Finally, the enabling technologies of federated vehicular networks (FVN) were outlined in [11], in which a highlevel architecture of FVN was discussed. For convenience, the related reported contributions are summarized in Table I.…”

Section: A Related Workmentioning

confidence: 99%

Towards Federated Learning-Enabled Visible Light Communication in 6G Systems

Naser¹,

Bariah²,

Muhaidat³

et al. 2021

Preprint

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Visible light communication (VLC) technology was introduced as a key enabler for the next generation of wireless networks, mainly thanks to its simple and low-cost implementation. However, several challenges prohibit the realization of the full potentials of VLC, namely, limited modulation bandwidth, ambient light interference, optical diffuse reflection effects, devices non-linearity, and random receiver orientation. On the contrary, centralized machine learning (ML) techniques have demonstrated a significant potential in handling different challenges relating to wireless communication systems. Specifically, it was shown that ML algorithms exhibit superior capabilities in handling complicated network tasks, such as channel equalization, estimation and modeling, resources allocation, and opportunistic spectrum access control, to name a few. Nevertheless, concerns pertaining to privacy and communication overhead when sharing raw data of the involved clients with a server constitute major bottlenecks in the implementation of centralized ML techniques. This has motivated the emergence of a new distributed ML paradigm, namely federated learning (FL), which can reduce the cost associated with transferring raw data, and preserve privacy by training ML models locally and collaboratively at the clients' side. Hence, it becomes evident that integrating FL into VLC networks can provide ubiquitous and reliable implementation of VLC systems. With this motivation, this is the first in-depth review in the literature on the application of FL in VLC networks. To that end, besides the different architectures and related characteristics of FL, we provide a thorough overview on the main design aspects of FL based VLC systems. Finally, we also highlight some potential future research directions of FL that are envisioned to substantially enhance the performance and robustness of VLC systems.

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“…[10] Threats models and major attacks in FL. Business [11] • Federated vehicular networks, their high-level architecture and enabling technologies.…”

Section: Reference Main Focus Applicationmentioning

confidence: 99%

Towards Federated Learning-Enabled Visible Light Communication in 6G Systems

Naser¹,

Bariah²,

Muhaidat³

et al. 2021

Preprint

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<div>Visible light communication is envisaged as a promising enabling technology for sixth generation (6G) and beyond networks. It was introduced as a key enabler for reliable massive-scale connectivity, mainly thanks to its simple and low-cost implementation which require minor variations to the existing indoor lighting systems. The key features of VLC allow offloading data traffic from the current congested radio frequency (RF) spectrum in order to achieve effective short-range, high speed, and green communications. However, several challenges prevent the realization of the full potentials of VLC, namely the limited modulation bandwidth of light emitting diodes, the interference resulted from ambient light, the effects of optical diffuse reflection, the non-linearity of devices, and the random receiver orientation. Meanwhile, centralized machine learning (ML) techniques have exhibited great potentials in handling different challenges in communication systems. Specifically, it has been recently shown that ML algorithms exhibit superior capabilities in handling complicated network tasks, such as channel equalization, estimation and modeling, resources allocation, opportunistic spectrum access control, non-linearity compensation, performance monitoring, detection, decoding/encoding, and network optimization. Nevertheless, concerns relating to privacy and communication overhead when sharing raw data of the involved clients with a server constitute major bottlenecks in large-scale implementation of centralized ML techniques. This has motivated the emergence of a new distributed ML paradigm, namely federated learning (FL). This method can reduce the cost associated with transferring the raw data, and preserve clients privacy by training ML model locally and collaboratively at the clients side. Thus, the integration of FL in VLC networks can provide ubiquitous and reliable implementation of VLC systems. Based on this, for the first time in the open literature, we provide an overview about VLC technology and FL. Then, we introduce FL and its integration in VLC networks and provide an overview on the main design aspects. Finally, we highlight some interesting future research directions of FL that are envisioned to boost the performance of VLC systems. </div>

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Federated Learning in Vehicular Networks: Opportunities and Solutions

Cited by 180 publications

References 10 publications

A Federated Learning Approach to Frequent Itemset Mining in Cyber-Physical Systems

A Federated Learning Approach to Frequent Itemset Mining in Cyber-Physical Systems

Towards Federated Learning-Enabled Visible Light Communication in 6G Systems

Towards Federated Learning-Enabled Visible Light Communication in 6G Systems

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