Next - Location Prediction Using Federated Learning on a Blockchain

Halim, Sadaf Md; Khan, Latifur; Thuraisingham, Bhavani

doi:10.1109/cogmi50398.2020.00038

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…This blockchain-assisted FL algorithm aims to predict cache files and enhance the cache hit rate. In the domain of location prediction, the scheme proposed in [135] utilized BFL for local training on users' mobile devices. This approach safeguards user privacy while making better use of the data for more accurate location predictions.…”

Section: Internet Of Vehiclesmentioning

confidence: 99%

A Survey on Blockchain-Based Federated Learning

Wu,

Ruan,

et al. 2023

Future Internet

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Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. In recent years, there have been notable advancements in combining these two technologies, particularly in data privacy protection, data sharing incentives, and computational performance. Although there are some surveys on blockchain-based federated learning (BFL), these surveys predominantly focus on the BFL framework and its classifications, yet lack in-depth analyses of the pivotal issues addressed by BFL. This work aims to assist researchers in understanding the latest research achievements and development directions in the integration of FL with blockchains. Firstly, we introduced the relevant research in FL and blockchain technology and highlighted the existing shortcomings of FL. Next, we conducted a comparative analysis of existing BFL frameworks, delving into the significant problems in the realm of FL that the combination of blockchain and FL addresses. Finally, we summarized the application prospects of BFL technology in various domains such as the Internet of Things, Industrial Internet of Things, Internet of Vehicles, and healthcare services, as well as the challenges that need to be addressed and future research directions.

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Section: Internet Of Vehiclesmentioning

confidence: 99%

A Survey on Blockchain-Based Federated Learning

Wu,

Ruan,

et al. 2023

Future Internet

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“…In [58], authors suggested a framework for utilizing data to boost position projections without jeopardizing the security of the clients who produce this data. The authors recommended using FL to practice remotely on a customer's smart devices while also detecting and combating the risk of harmful operators or enemies intentionally reporting inappropriate information to harm the training method.…”

Section: Blockchain-based Federated Transfer Learning Techniquesmentioning

confidence: 99%

Blockchain-based federated learning methodologies in smart environments

2021

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“…However, after a systematic analysis of the literature, we observed that there are problems that have not yet been addressed or that need to be studied in greater depth. In the first place, most of the studies propose approaches that do not consider the privacy problems involved in exposing sensitive user data [17], such as their geographical location and the information about the places they visit. Second, existent approaches use static parameters for the detection of stays and places.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Approach for Building User Mobility Profiles

Vallejos,

Berdun,

Armentano

et al. 2023

IJGI

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Data captured by mobile devices enable us, among other things, learn the places where users go, identify their home and workplace, the places they usually visit (e.g., supermarket, gym, etc.), the different paths they take to move from one place to another and even their routines. In summary, with this information, it is possible to learn a user mobility profile. In this work, we propose a lightweight approach for building mobility profiles from data collected with mobile devices. The mobility profiles of a user consist of the places visited, the visit history and the travel paths. Our approach aims to solve some of the challenges and limitations identified in the literature. Particularly, it considers geographic information to identify certain kinds of places, such as open spaces, big places and small places, that are hard to distinguish with existing approaches. We use different sensors and time frequencies to collect data in order to optimize battery consumption and maximize precision. Finally, it executes entirely on the mobile devices, avoiding the exposure of sensitive user information and then preserving user privacy. The proposal was evaluated in the context of the real usage of the developed prototype applications in two cities of Argentina. The results obtained with our approach outperformed other approaches in the literature, both in precision and recall.

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Next - Location Prediction Using Federated Learning on a Blockchain

Cited by 12 publications

References 16 publications

A Survey on Blockchain-Based Federated Learning

A Survey on Blockchain-Based Federated Learning

Blockchain-based federated learning methodologies in smart environments

A Lightweight Approach for Building User Mobility Profiles

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