Lightweight Federated Learning for Large-Scale IoT Devices With Privacy Guarantee

Wei, Zhaohui; Pei, Qingqi; Zhang, Ning; Li, Xuefeng; Wu, Celimuge; Taherkordi, Amir

doi:10.1109/jiot.2021.3127886

Cited by 27 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Federated learning is an emerging technology that combines big data processing and artificial intelligence. The idea of federated learning is to break the data islands to ensure the information security of all parties [17]- [18]. The entire work-flow of federated learning can be divided into several steps.…”

Section: Distributed Federated Learningmentioning

confidence: 99%

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

Liu

Chen

et al. 2023

IEEE Trans. on Green Commun. Netw.

View full text Add to dashboard Cite

show abstract

Section: Distributed Federated Learningmentioning

confidence: 99%

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

Liu

Chen

et al. 2023

IEEE Trans. on Green Commun. Netw.

View full text Add to dashboard Cite

show abstract

“…There are also many lightweight federated schemes. A lightweight privacy-preserving scheme of FL is proposed by Wei et al [73] to protect the security of all local data. To deal with large-scale FL tasks, a secure mask-reusing mechanism is also designed.…”

Section: Challengementioning

confidence: 99%

Federated Learning for Metaverse: A Survey

Chen

Huang

Gan

et al. 2023

Companion Proceedings of the ACM Web Conference 2023

View full text Add to dashboard Cite

The metaverse, which is at the stage of innovation and exploration, faces the dilemma of data collection and the problem of private data leakage in the process of development. This can seriously hinder the widespread deployment of the metaverse. Fortunately, federated learning (FL) is a solution to the above problems. FL is a distributed machine learning paradigm with privacy-preserving features designed for a large number of edge devices. Federated learning for metaverse (FL4M) will be a powerful tool. Because FL allows edge devices to participate in training tasks locally using their own data, computational power, and model-building capabilities. Applying FL to the metaverse not only protects the data privacy of participants but also reduces the need for high computing power and high memory on servers. Until now, there have been many studies about FL and the metaverse, respectively. In this paper, we review some of the early advances of FL4M, which will be a research direction with unlimited development potential. We first introduce the concepts of metaverse and FL, respectively. Besides, we discuss the convergence of key metaverse technologies and FL in detail, such as big data, communication technology, the Internet of Things, edge computing, blockchain, and extended reality. Finally, we discuss some key challenges and promising directions of FL4M in detail. In summary, we hope that our up-to-date brief survey can help people better understand FL4M and build a fair, open, and secure metaverse. CCS CONCEPTS• Computing methodologies → Federated learning.

show abstract

“…Yeom et al [8] proposed an energy-efficient SAGIN based on federated learning, in which IoT devices choose appropriate satellites or UAVs for task offloading. Wei et al [9] proposed a lightweight privacypreserving federated learning scheme for large-scale IoT devices. The authors presented a reusable masking with the secret-sharing protocol to protect the privacy of individual local data while reducing the computing and communication overhead.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning with Privacy-Preserving Incentives for Aerial Computing Networks

Wang,

Yang,

Sun

et al. 2024

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

With the help of artificial intelligence (AI) model, aerial computing can help analyze and predict the network dynamics and support intelligent decision-making to improve the performance of 6G space-air-ground integrated networks. Federated learning has been proposed to tackle the challenges of limited energy and data shortage for the application of AI models in aerial computing networks. A critical problem of FL for aerial computing is the lack of incentives due to privacy concerns. On the one hand, the information needed to measure users' learning quality may be eavesdropped. On the other hand, users' real costs for determining payments may also undertake inference attacks. In this paper, we design a privacy-preserving and learning quality-aware incentive mechanism for federated learning in aerial computing networks. We propose differential privacy based scheme to protect the privacy of the real cost. In addition, utilize Combinatorial Multi-Armed Bandit (CMAB) algorithm to evaluate the user learning quality without any participant information. Simulation results demonstrate that our scheme can significantly motivate high-quality participants with guaranteed privacy preservation and achieve effective federated learning under the constraint of the limited budget.

show abstract

Lightweight Federated Learning for Large-Scale IoT Devices With Privacy Guarantee

Cited by 27 publications

References 31 publications

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

Federated Learning for Metaverse: A Survey

Federated Learning with Privacy-Preserving Incentives for Aerial Computing Networks

Contact Info

Product

Resources

About