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Communication-Efficient Distributed Online Learning with Kernels
[article]
2019
arXiv
pre-print
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We propose an efficient distributed online learning protocol for low-latency real-time services. ...
It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion. ...
of a distributed online learning system. ...
arXiv:1911.12899v1
fatcat:rg3ojhwrf5hdtfoy7x4r2wz7b4
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey
[article]
2021
arXiv
pre-print
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Furthermore, we review the distributed GPs with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. ...
Since GPs achieve the expressive and interpretable learning ability with uncertainty, it is particularly suitable for wireless communication. ...
Another deep kernel using the finite rank Mercer kernel function with orthogonal embeddings on the last layer has a better learning efficiency and expressiveness [85] . ...
arXiv:2103.10134v3
fatcat:bhox7nbavvcb7lnzndu2zr44r4
QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM
[article]
2022
arXiv
pre-print
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We then propose a novel learning framework named Online Decentralized Kernel learning via Linearized ADMM (ODKLA) to efficiently solve the online decentralized kernel learning problem. ...
This paper focuses on online kernel learning over a decentralized network. ...
online kernel learning to efficiently solve the online kernel learning problem over a decentralized network. ...
arXiv:2208.02777v1
fatcat:pwwv4332fnh3zhm4vhpmqo3f2u
A Survey on Dynamic Structure Embedded Online Multiple-Output Regression for Streaming Data
2020
International Journal for Research in Applied Science and Engineering Technology
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In this way online multiple-output regression uses the techniques of machine learning system for modeling correlated data stream and predicting multidimensional related data stream and it always provides ...
It can work with various side information sources that are communicated as different kernels. ...
Kernelized matrix factorization extends with full Bayesian treatment and it can work with various side data sources that are communicated at different kernels. ...
doi:10.22214/ijraset.2020.5427
fatcat:7hgozu4wbjey7nfxv3mf4j4qju
Optimally Compressed Nonparametric Online Learning
[article]
2020
arXiv
pre-print
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Unfortunately, when used online, nonparametric methods suffer a "curse of dimensionality" which precludes their use: their complexity scales at least with the time index. ...
Further, the applications to robotics, communications, economics, and power are discussed, as well as extensions to multi-agent systems. ...
Online multi-agent learning with nonlinear models may pave the pathway for next-generation distributed intelligence. ...
arXiv:1909.11555v2
fatcat:z2tl34d7nbazdlzfmwrsw5rzfu
Are we still friends: Kernel multivariate survival analysis
2014
2014 IEEE Global Communications Conference
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Furthermore, to avoid the high computational complexity in kernel learning we impose sparsity in our model. ...
Therefore, precisely modeling and predicting state of each online relationship is worthwhile in many respects. For social communication services such modeling permits new and novel online services. ...
Although a support vector machine learning method with kernel function was proposed to fit in nonlinear dependencies, a learning algorithm with computational complexity up to O(n 3 ) and memory requirement ...
doi:10.1109/glocom.2014.7036842
dblp:conf/globecom/LiangLCMWSTW14
fatcat:hdsts7w5zzfmfm5lteisgm7qaq
Communication-Efficient Online Federated Learning Framework for Nonlinear Regression
[article]
2021
arXiv
pre-print
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Experimental results show that PSO-Fed can achieve competitive performance with a significantly lower communication overhead than Online-Fed. ...
Some recent works introduced a framework for online FL (Online-Fed) wherein clients perform model learning on streaming data and communicate the model to the server; however, they do not address the associated ...
Our solution to this problem stems from a different approach, namely, partial-sharing concepts [24, 25] that are very attractive for communication-efficient distributed learning. ...
arXiv:2110.06556v1
fatcat:iirn6aqp4rg53ofe4vtqirtgty
Distributed Online Learning with Multiple Kernels
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2102.12733v1 fatcat:5uoat7lppnafzfpmh2akwcy47e
For this model, we propose a novel learning framework with multiple kernels, which is named DOMKL. ...
This learning model is called a fully distributed online learning (or a fully decentralized online federated learning). ...
Thus, it is still an open problem to construct an efficient multiple kernel-based algorithm for distributed online learning tasks, which is the primary motivation of this paper. ...
arXiv:2102.12733v2
fatcat:f33baqmi3fd7heprbyhcbsjja4
2019 Index IEEE Transactions on Signal and Information Processing over Networks Vol. 5
2019
IEEE Transactions on Signal and Information Processing over Networks
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., +, TSIPN Dec. 2019 779-791
Asynchronous Online Learning in Multi-Agent Systems With Proximity
Constraints. ...
Yan, J., +, TSIPN March 2019 168-180
L
Learning (artificial intelligence)
Asynchronous Online Learning in Multi-Agent Systems With Proximity
Constraints. ...
doi:10.1109/tsipn.2019.2959414
fatcat:ixpx5rg5l5hshkt2ppvie3afqe
Meta-Learning Priors for Efficient Online Bayesian Regression
[article]
2018
arXiv
pre-print
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space yields accurate online predictions of the posterior predictive density. ...
scalability and data-efficiency are important. ...
While the ALPaCA model has shown potential for efficient online Bayesian learning, it has several limitations. ...
arXiv:1807.08912v2
fatcat:l2m2oohlyzdrxbcsk7a2t5piii
Online feature extraction for the incremental learning of gestures in human-swarm interaction
2014
2014 IEEE International Conference on Robotics and Automation (ICRA)
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To learn and classify gestures in an online and incremental fashion, we employ a 2nd order online learning method, namely the Soft-Confidence Weighted (SCW) learning scheme. ...
We present a novel approach for the online learning of hand gestures in swarm robotic (multi-robot) systems. ...
The authors participate in Project 4: Distributed Robotics, sub-project 4.5, Symbiotic human-swarm cooperation. ...
doi:10.1109/icra.2014.6907338
dblp:conf/icra/NagiGNGC14
fatcat:7menzlmu6zh4rc5ckhvahdga7m
Data-driven online variational filtering in wireless sensor networks
2009
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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Index Terms-Bayesian filtering, sensor networks, machine learning. ...
Based on a few selected sensors, target tracking is performed distributively without any information about the observation model. ...
Following the kernel trick, commonly used in the machine learning community, the similarity measurements are considered as scalar products in a reproducing kernel Hilbert space (RKHS). ...
doi:10.1109/icassp.2009.4960108
dblp:conf/icassp/SnoussiTDR09
fatcat:jj6alrg6g5f5xg5voi6qhn5yuy
Kernel-based Online Learning for Real-Time Voltage Control in Distribution Networks
2020
IET Smart Grid
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Specifically, the proposed datadriven approach uses functional stochastic gradient descent in Reproducing Kernel Hilbert Spaces (RKHSs), to learn the control strategies for Distributed Generation (DG) ...
This paper presents a new data-driven voltage control approach for distribution networks based on kernel methods. ...
Online learning with kernels has not yet being proposed to learn strategies for controlled assets. ...
doi:10.1049/iet-stg.2019.0312
fatcat:j5xcoiybxja4dgnhppiazc3y4q
Kernel Association for Classification and Prediction: A Survey
2015
IEEE Transactions on Neural Networks and Learning Systems
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This survey outlines the latest trends and innovations of a kernel framework for big data analysis. KA topics include offline learning, distributed database, online learning, and its prediction. ...
techniques for nonlinear optimal filter implementations. 3) Explore kernel selection for distributed databases including solutions of heterogeneous issues. ...
DISTRIBUTED DATABASE WITH KERNEL Various KAs using offline learning may be extended to address big data by considering distributed databases. ...
doi:10.1109/tnnls.2014.2333664
pmid:25029489
fatcat:cotcvbtpk5fcpkwgwpg26b6clu
Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system
2020
Science China Information Sciences
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Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system. ...
Visible light communication (VLC) network over optical fiber has become a potential candidate in ultra-high speed indoor wireless communication. ...
To establish efficient machine learning model, besides adjusting the parameters of SVM, size of training set is also critical. ...
doi:10.1007/s11432-019-2850-3
fatcat:cj2frxopnbdhrmc53qh6kpwyw4
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