Towards Federated Learning Approach to Determine Data Relevance in Big Data

Doku, Ronald; Rawat, Danda B.; Liu, Chunmei

doi:10.1109/iri.2019.00039

Cited by 34 publications

(12 citation statements)

References 8 publications

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“…Zhou et al [49] propose a FL-based real-time data processing architecture for multi-robot systems. Doku et al [50] uses a combination of FL with blockchain to determine data relevance and store relevant data in a decentralized manner.…”

Section: A Application Of Fl For Wireless Iotmentioning

confidence: 99%

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Yoshinaga

et al. 2020

IEEE Open J. Comput. Soc.

264

106

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Section: A Application Of Fl For Wireless Iotmentioning

confidence: 99%

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Yoshinaga

et al. 2020

IEEE Open J. Comput. Soc.

264

106

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“…Doku et. al in [194] applied the principle of FL to determine the data relevance in big data. In the nearest future, the principle of federated learning is therefore expected to be used to secure systems using ML.…”

Section: Federated Learning (Fl) In Cybersecuritymentioning

confidence: 99%

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Olowononi

Rawat

Liu

2021

IEEE Commun. Surv. Tutorials

Self Cite

132

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Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

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“…In federated learning, the model is distributed to decentralized devices for training as well as prediction (T. Li et al, 2019), and the parameters are brought over to the master server for aggregation. In such a scenario, it is also advantageous to apply the machine learning process to low‐cost devices, such as mobile phones and PDA (Doku et al, 2019).…”

Section: Preliminariesmentioning

confidence: 99%

A survey on federated learning in data mining

Mao

et al. 2021

WIREs Data Min & Knowl

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Data mining is a process to extract unknown, hidden, and potentially useful information from data. But the problem of data island makes it arduous for people to collect and analyze scattered data, and there is also a privacy security issue when mining data. A collaboratively decentralized approach called federated learning unites multiple participants to generate a shareable global optimal model and keeps privacy‐sensitive data on local devices, which may bring great hope to us for solving the problems of decentralized data and privacy protection. Though federated learning has been widely used, few systematic studies have been conducted on the subject of federated learning in data mining. Hence, different from prior reviews in this field, we make a comprehensive summary and provide a novel taxonomy of the application of federated learning in data mining. This article starts by providing a thorough description of the relevant definitions and concepts, followed by an in‐depth investigation on the challenges faced by federated learning. In this context, we elaborate four taxonomies of major applications of federated learning in data mining, including education, healthcare, IoT, and intelligent transportation, and discuss them comprehensively. Finally, we discuss four promising research directions for further research, that is, privacy enhancement, improvement of communication efficiency, heterogeneous system processing, and reducing economic costs. This article is categorized under: Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence

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Towards Federated Learning Approach to Determine Data Relevance in Big Data

Cited by 34 publications

References 8 publications

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

A survey on federated learning in data mining

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