A Unified Federated Learning Framework for Wireless Communications: towards Privacy, Efficiency, and Security

Wen, Hui; Wu, Yue; Yang, Chenming; Huang, Duan; Yu, Shui

doi:10.1109/infocomwkshps50562.2020.9162672

Cited by 25 publications

(9 citation statements)

References 4 publications

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“…A wireless protocol for FD and its enhanced version are studied in [130]. Moreover, FD can be applied simultaneously with other techniques, and [131] introduces a two-step joint learning framework, robust federated augmentation and distillation (RFA-RFD), which improves communication efficiency while preserving data privacy and resisting Byzantine devices.…”

Section: A Communication Costmentioning

confidence: 99%

“…A distributed gradient descent algorithm proposed in [156] performs a simple thresholding based on the gradient parametrization to mitigate the failure of blocking Byzantine style. A two-step learning framework is proposed in [157] that generates independent same-distribution datasets at edge devices and only requires uploading local model output information, reducing private data uploads and achieving robustness to Byzantine attacks.…”

Section: Security and Robustnessmentioning

confidence: 99%

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Distributed Learning for Wireless Communications: Methods, Applications and Challenges

Qian

Yang

Xiao

et al. 2022

IEEE J. Sel. Top. Signal Process.

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With its privacy-preserving and decentralized features, distributed learning plays an irreplaceable role in the era of wireless networks with a plethora of smart terminals, an explosion of information volume and increasingly sensitive data privacy issues. There is a tremendous increase in the number of scholars investigating how distributed learning can be employed to emerging wireless network paradigms in the physical layer, media access control layer and network layer. Nonetheless, researches on distributed learning for wireless communications are still in its infancy. In this paper, we review the contemporary technical applications of distributed learning for wireless communications. We first introduce the typical frameworks and algorithms for distributed learning. Examples of applications of distributed learning frameworks in the emerging wireless network paradigms are then provided. Finally, main research directions and challenges of distributed learning for wireless communications are discussed.

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Section: A Communication Costmentioning

confidence: 99%

Section: Security and Robustnessmentioning

confidence: 99%

Distributed Learning for Wireless Communications: Methods, Applications and Challenges

Qian

Yang

Xiao

et al. 2022

IEEE J. Sel. Top. Signal Process.

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“…The introduction of the machine learning (ML) algorithm is gaining more attraction because of its complex modeling capability from large datasets stored on a central server [29]. In traditional ML, the data is mostly stored at a central location without considering any privacy prevention countermeasures and data transmission costs.…”

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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“…Industries (e.g., manufacturing, logistics, and transportation) often face even more serious data threats due to owning a vast amount of valuable information, so they possess the most urgent and critical requirement to increase security to protect data. Therefore, any form of disclosure [10]- [13] or tampering [14]- [16] of the confidential raw data is not allowed. 2) Rapidly changing streaming data on data-intensive sensors.…”

Section: Introductionmentioning

confidence: 99%

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

Li¹,

He²,

Yu³

et al. 2022

Preprint

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Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate on decentralized devices in modern factories. To protect the confidentiality of industrial data, federated learning (FL) was introduced to collaboratively train shared machine learning models. However, the local data collected by different devices skew in class distribution and degrade industrial FL performance. This challenge has been widely studied at the mobile edge, but they ignored the rapidly changing streaming data and clustering nature of factory devices, and more seriously, they may threaten data security. In this paper, we propose FEDGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i.i.d. data. Taking advantage of naturally clustered factory devices, FEDGS uses a gradient-based binary permutation algorithm (GBP-CS) to select a subset of devices within each factory and build homogeneous super nodes participating in FL training. Then, we propose a compound-step synchronization protocol to coordinate the training process within and among these super nodes, which shows great robustness against data heterogeneity. The proposed methods are time-efficient and can adapt to dynamic environments, without exposing confidential industrial data in risky manipulation. We prove that FEDGS has better convergence performance than FedAvg and give a relaxed condition under which FEDGS is more communication-efficient. Extensive experiments show that FEDGS improves accuracy by 3.5% and reduces training rounds by 59% on average, confirming its superior effectiveness and efficiency on non-i.i.d. data.

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A Unified Federated Learning Framework for Wireless Communications: towards Privacy, Efficiency, and Security

Cited by 25 publications

References 4 publications

Distributed Learning for Wireless Communications: Methods, Applications and Challenges

Distributed Learning for Wireless Communications: Methods, Applications and Challenges

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

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

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