BAFL: A Blockchain-Based Asynchronous Federated Learning Framework

Feng, Lei; Zhao, Yiqi; Guo, Shaoyong; Qiu, Xuesong; Li, Wenjing; Yu, Peng

doi:10.1109/tc.2021.3072033

Cited by 86 publications

(45 citation statements)

References 30 publications

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“…However, traditional FL has privacy issues as local model updation depends upon central model parameters. If the central global model is compromised, the overall efficiency of the whole FL framework suffers [36]- [38]. Moreover, there are constraints associated with individual IoT devices, such as power and resources, limiting the FL operation.…”

Section: Fl and Its Perspective In Iomtmentioning

confidence: 99%

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali¹,

Naeem²,

Tariq³

et al. 2022

Preprint

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Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training approach of artificial intelligence (AI), mobile and wearable IoMT devices raise privacy issues concerning the information communicated between hospitals and end-users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm, has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to endusers as only gradients are shared during training. For these specific properties of FL, in this paper, we present privacyrelated issues in IoMT. Afterward, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Moreover, we present some practical opportunities of FL in IoMT. In the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems.

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Section: Fl and Its Perspective In Iomtmentioning

confidence: 99%

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali¹,

Naeem²,

Tariq³

et al. 2022

Preprint

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show abstract

“…Initiatives towards collaborative model development approaches using AI and ML techniques have given rise to Federated Learning (FL), in which individual data storage on a decentralized network is encouraged [81][82][83]. This facilitates predictive data analysis through training of a shared model using different data sets, offering capabilities of generalized model development with the benefit of privacy preservation of the user data.…”

Section: Future Directionsmentioning

confidence: 99%

Can Blockchain Strengthen the Energy Internet?

Yapa

Alwis

Liyanage

2021

Network

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Emergence of the Energy Internet (EI) demands restructuring of traditional electricity grids to integrate heterogeneous energy sources, distribution network management with grid intelligence and big data management. This paradigm shift is considered to be a breakthrough in the energy industry towards facilitating autonomous and decentralized grid operations while maximizing the utilization of Distributed Generation (DG). Blockchain has been identified as a disruptive technology enabler for the realization of EI to facilitate reliable, self-operated energy delivery. In this paper, we highlight six key directions towards utilizing blockchain capabilities to realize the envisaged EI. We elaborate the challenges in each direction and highlight the role of blockchain in addressing them. Furthermore, we summarize the future research directive in achieving fully autonomous and decentralized electricity distribution networks, which will be known as Energy Internet.

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“…Other related works in [29], [30] proposed blockchain-based FL schemes for health Internet of Things (IoT) networks, but their roles in COVID-19 detection have not been investigated. The studies in [31], [32] also paid attention to blockchain-based FL designs, where the proposed solutions were used to mostly address attack issues in the data communication and model aggregation. Nonetheless, all existing works [26]- [29], [31], [32] have not addressed the latency issue caused by blockchain mining in the blockchain-FL systems.…”

Section: A Related Workmentioning

confidence: 99%

“…The studies in [31], [32] also paid attention to blockchain-based FL designs, where the proposed solutions were used to mostly address attack issues in the data communication and model aggregation. Nonetheless, all existing works [26]- [29], [31], [32] have not addressed the latency issue caused by blockchain mining in the blockchain-FL systems. Moreover, the integrated design of blockchain, FL, and GANs and the investigation of this integrated model in the COVID-19 context are still missing in the above literature studies.…”

Section: A Related Workmentioning

confidence: 99%

Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing

Nguyen,

Ding,

Pathirana

et al. 2021

Preprint

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COVID-19 has spread rapidly across the globe and become a deadly pandemic. Recently, many artificial intelligencebased approaches have been used for COVID-19 detection, but they often require public data sharing with cloud datacentres and thus remain privacy concerns. This paper proposes a new federated learning scheme, called FedGAN, to generate realistic COVID-19 images for facilitating privacy-enhanced COVID-19 detection with generative adversarial networks (GANs) in edge cloud computing. Particularly, we first propose a GAN where a discriminator and a generator based on convolutional neural networks (CNNs) at each edge-based medical institution alternatively are trained to mimic the real COVID-19 data distribution. Then, we propose a new federated learning solution which allows local GANs to collaborate and exchange learned parameters with a cloud server, aiming to enrich the global GAN model for generating realistic COVID-19 images without the need for sharing actual data. To enhance the privacy in federated COVID-19 data analytics, we integrate a differential privacy solution at each hospital institution. Moreover, we propose a new blockchain-based FedGAN framework for secure COVID-19 data analytics, by decentralizing the FL process with a new mining solution for low running latency. Simulations results demonstrate the superiority of our approach for COVID-19 detection over the state-of-the-art schemes.

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BAFL: A Blockchain-Based Asynchronous Federated Learning Framework

Cited by 86 publications

References 30 publications

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Can Blockchain Strengthen the Energy Internet?

Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing

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