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Low-Latency Federated Learning over Wireless Channels with Differential Privacy
[article]
2021
arXiv
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In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement. ...
In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. ...
Differential Privacy The DP mechanism with parameters and provides a strong criterion for the privacy preservation of distributed data processing systems. ...
arXiv:2106.13039v2
fatcat:nxpj6wzfsvaqzjii534zbcsqgu
Federated Learning and Wireless Communications
[article]
2020
arXiv
pre-print
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for training a federated learning model, and federated learning for intelligent wireless applications. ...
Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful functions and potential applications. ...
and low latency, which is referred to as ultra-reliable low-latency communication (URLLC). ...
arXiv:2005.05265v2
fatcat:psj4p3redrg43kgn53xof6d7wq
Wireless Federated Learning (WFL) for 6G Networks – Part I: Research Challenges and Future Trends
[article]
2021
arXiv
pre-print
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, termed as Wireless Federated Learning (WFL). ...
Recently, the massive volume of generated wireless data, the privacy concerns and the increasing computing capabilities of wireless end-devices have led to the emergence of a promising decentralized solution ...
Among the decentralized approaches, federated learning (FL) has been proposed as a promising solution for protecting the data privacy and meeting the low-latency demands [2] , [3] . ...
arXiv:2105.00842v1
fatcat:nbeh2ozel5h3zcztqeba7lft7q
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications
[article]
2021
arXiv
pre-print
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In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. ...
However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion ...
grasp learned using a tactile glove [319] ) over the Tactile Internet in an ultra-reliable and low-latency manner. ...
arXiv:2111.12444v1
fatcat:crrbtfylvjeihogumggdnxcbpq
Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges
[article]
2020
arXiv
pre-print
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Owing to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. ...
for future research on federated learning in the context of wireless communications. ...
Since local learners and the aggregator need to exchange model parameters over the wireless channel, this would give rise to the paradigm of federated learning with parameter quantization. ...
arXiv:1908.06847v4
fatcat:plfaupfexzd5bb3o72f3z5kskm
Securing Federated Learning: A Covert Communication-based Approach
[article]
2021
arXiv
pre-print
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Federated Learning Networks (FLNs) have been envisaged as a promising paradigm to collaboratively train models among mobile devices without exposing their local privacy data. ...
Balancing privacy protection with efficient distributed model training is a key challenge for FLNs. ...
COVERT COMMUNICATION-BASED FEDERATED LEARNING
A. ...
arXiv:2110.02221v1
fatcat:h6arwvyi5ncdvknzbl4olrceha
Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems
2020
IEEE Access
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Due to its privacy-preserving and low communication overhead and latency, FDL is much more adequate for many UAVs-enabled wireless applications. ...
INDEX TERMS Deep learning, federated deep learning, UAVs-based wireless networks, wireless communications. ...
It also introduces a lower latency to generate learning models and hence supports the ultra low latency requirement of the emergent UAV-based applications. ...
doi:10.1109/access.2020.2981430
fatcat:7jbg7xcav5bifiniyxyubq4s7y
Guest Editorial Special Issue on Distributed Learning Over Wireless Edge Networks—Part I
2021
IEEE Journal on Selected Areas in Communications
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With that said, distributed training still requires a substantial amount of information exchange between devices and edge servers over wireless links. ...
The avoidance of raw-data uploading not only helps to preserve privacy but may also alleviate network-traffic congestion and minimize latency. ...
In [A15] , Seif et al. consider the optimization of training federated stochastic gradient descent (FedSGD) over fading multiple access channels, subject to central and local differential privacy constraints ...
doi:10.1109/jsac.2021.3118484
fatcat:potsxdjmfje4njbhrb2obwgfpu
Editorial: Introduction to the Issue on Distributed Machine Learning for Wireless Communication
2022
IEEE Journal on Selected Topics in Signal Processing
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The sixteenth paper, entitled "Loss-Privacy Tradeoff in Federated Edge Learning," proposes a personalized differential privacy based federated mobile edge learning (FMEL) scheme to alleviate the privacy ...
with only local channel state information (CSI) as input. ...
doi:10.1109/jstsp.2022.3165356
fatcat:dab46w4tone55oow6gquetnp6m
Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing
[article]
2022
arXiv
pre-print
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To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. ...
Also, recent achievements of enabling techniques for the dual-functional design are surveyed with exemplifications from the mutual perspectives of "communications for learning" and "learning for communications ...
In particular, the authors first proved that channel noise can be used to achieve differential privacy in FL. ...
arXiv:2206.00422v1
fatcat:osp426emrngi3bvye6fmk7kqce
Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise
[article]
2020
arXiv
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Over-the-air computation (AirComp)-based federated learning (FL) enables low-latency uploads and the aggregation of machine learning models by exploiting simultaneous co-channel transmission and the resultant ...
To understand this tradeoff more fully, the closed-form expressions of SNR (with respect to the privacy level) are derived, and the tradeoff is analytically demonstrated. ...
Regarding the first issue of upload latency over wireless multiple-access channels, over-the-air computation (AirComp) has emerged as a promising approach [6] - [10] . ...
arXiv:2004.06337v1
fatcat:av3xtswor5euxewli5jfaf5cgm
Knowledge Distillation For Wireless Edge Learning
[article]
2021
arXiv
pre-print
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Using this close-to-practice dataset, we find that widely used federated learning approaches, specially those that are privacy preserving, are worse than local training for a wide range of settings. ...
The proposed framework achieves overall better performance than both local and federated training approaches, while being robust against catastrophic failures as well as challenging channel conditions ...
g t+1 : g t+1 = N i=1 w i N . (1) Differential Privacy Federated Averaging (DP-Fed): is the original FedAvg technique incorporating differential privacy as a solution to solve the privacy issue. ...
arXiv:2104.06374v1
fatcat:4frmsvmnwnaxvehddvhbeob4na
Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning
[article]
2022
arXiv
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Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile ...
However, model communication over wireless channels, especially in uplink model uploading of FEEL, has been widely recognized as a bottleneck that critically limits the efficiency of FEEL. ...
With RIS, the wireless channel can be configured to achieve diverse system requirements at a low cost by optimizing the RIS phase shifts. ...
arXiv:2109.02353v2
fatcat:fpi7xru5tfghvbddd3z3teb4ba
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks
[article]
2020
arXiv
pre-print
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There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices ...
It considers a multi-layer hybrid learning framework consisting of heterogeneous devices with various proximities. ...
These physical/link-layer technologies can be developed jointly with fog learning to conduct model training over large numbers of users with high data rates and low latency. 12) Deep reinforcement learning ...
arXiv:2006.03594v3
fatcat:mpcav4qexvgwdmnvvr4qzuiblm
Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities
[article]
2022
arXiv
pre-print
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learning, so as to enable the smart applications. ...
., user-defined quality of experience metrics, latency, and reliability) that are challenging to be fulfilled by existing wireless systems. ...
In over-the-air computation, the channel noise is considered a differential privacy noise for preserving the distributed learning privacy. ...
arXiv:2202.02559v1
fatcat:v6afo5n2srfqle3se3zbjarzeq
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