FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things

Yin, Bo; Yin, Hao; Wu, Yulei; Jiang, Zexun

doi:10.1109/jiot.2020.2966778

Cited by 124 publications

(40 citation statements)

References 38 publications

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“…data collaboration framework is studied in [58] for federated data learning among multiple parties, including public data center and private data center, enabled by a blockchain ledger. The data usage event and FL updates are offloaded to the blockchain before transmitting to the central server for secure aggregation, while the data control of users is ensured.…”

Section: Deep Reinforcementmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

698

182

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Section: Deep Reinforcementmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

698

182

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“…In doing so, we can obtain an overall effective model by collecting all the model weights that were collected from the local models. However, this approach would likely need to be modified for medical communities [46]. Different devices want to form their own models based on their environment and resource capacities.…”

Section: Architectures and Platforms Of Federated Learningmentioning

confidence: 99%

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

et al. 2020

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This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

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“…ECNs can also be deployed at IoT/IIoT gateways for industrial scenarios, providing data collection and aggregation functionalities. ECNs can even be deployed at the end devices, performing data pre-processing [63], [64].…”

Section: B Edge Computingmentioning

confidence: 99%

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Dai

Wang

2021

IEEE Internet Things J.

Self Cite

228

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Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to ever-increasing number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this paper, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on state-of-the-art for security and privacy, and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

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FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things

Cited by 124 publications

References 38 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

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