A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression
[article]
2021
arXiv
pre-print
Preserved Fulltext
To reduce the communication overhead associated with asynchronous online federated learning (ASO-Fed), we use the principles of partial-sharing-based communication. ...
An asynchronous setting reflects the realistic environment in which federated learning methods must be able to operate reliably. ...
The proposed partial-sharing asynchronous online federated learning (PAO-Fed) algorithm oversees the collaborative estimation of a continuous nonlinear model represented on a random Fourier feature (RFF ...
arXiv:2111.13931v1
fatcat:gmh3c4pcsba7llfwfpufeey25e
Table of Contents
2021
IEEE Transactions on Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Willett Robust Power Allocation for Resource-Aware Multi-Target Tracking With Colocated MIMO Radars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Chen UVeQFed: Universal Vector Quantization for Federated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..Cui Measurement Bounds for Compressed ...
doi:10.1109/tsp.2021.3136800
fatcat:zhf46mb3rbdlnnh3u2xizgxof4
Table of Contents
2020
IEEE Transactions on Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Zhang 4041 Optimal Resource Allocation for Asynchronous Multiple Targets Tracking in Heterogeneous Radar Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Cecchi 5308 (Contents Continued on Page xiii) 1558 Risk Convergence of Centered Kernel Ridge Regression With Large Dimensional Data
1589 Resource Scheduling for Distributed Multi-Target Tracking in ...
doi:10.1109/tsp.2020.3042287
fatcat:nh7viihaozhd7li3txtadnx5ui
2020 Index IEEE Transactions on Signal Processing Vol. 68
2020
IEEE Transactions on Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
., One-Step Prediction for Discrete Time-Varying Nonlinear Systems With Unknown Inputs and Correlated Noises; TSP ...
., +, TSP 2020 6509-6520 Optimal Resource Allocation for Asynchronous Multiple Targets Tracking in Heterogeneous Radar Networks. ...
., TSP 2020 3871-3886
Optimal Resource Allocation for Asynchronous Multiple Targets Tracking in
Heterogeneous Radar Networks. ...
doi:10.1109/tsp.2021.3055469
fatcat:6uswtuxm5ba6zahdwh5atxhcsy
Table of Contents [EDICS]
2020
IEEE Transactions on Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Yarovoy 6562 Machine Learning Fast Adaptive Gradient RBF Networks For Online Learning of Nonstationary Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Ting 6394 Accelerated Structure-Aware Reinforcement Learning for Delay-Sensitive Energy Harvesting Wireless Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Herzet 3252 Distributed Constrained Online Learning . . . . . . . . . . . . . . . . . . . . . . . . S. Paternain, S. Lee, M. M. Zavlanos, and A. ...
doi:10.1109/tsp.2020.3045363
fatcat:wcnvdcy3rvhblh7rtxfe6gz4re
Machine learning for intelligent optical networks: A comprehensive survey
2020
Journal of Network and Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2003.05290v1 fatcat:wzp7avtigrgutlnzcazru2zirm
It is imperative to improve intelligence in communication network, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML). ...
The applications of ML are classified in terms of their use cases, which are categorized into optical network control and resource management, and optical networks monitoring and survivability. ...
Machine learning for Intelligent Optical Networks Monitoring and Survivability The awareness of the network states and performance, which provides essential information for network control and management ...
doi:10.1016/j.jnca.2020.102576
fatcat:yxrh4phho5d5hggqwwh7sino3i
2020 Index IEEE Internet of Things Journal Vol. 7
2020
IEEE Internet of Things Journal
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
., Rateless-Code-Based Secure Cooperative Transmission Scheme for Industrial IoT; JIoT July 2020 6550-6565 Jamalipour, A., see Murali, S., JIoT Jan. 2020 379-388 James, L.A., see Wanasinghe, T.R., ...
., +, JIoT April 2020 2501-2508 PADL: Privacy-Aware and Asynchronous Deep Learning for IoT Applications. ...
., +, JIoT Jan. 2020 220-233 Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT. ...
doi:10.1109/jiot.2020.3046055
fatcat:wpyblbhkrbcyxpnajhiz5pj74a
Network-Aware Optimization of Distributed Learning for Fog Computing
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2004.08488v2 fatcat:7m6feezydfeftdvpoayda26bhiOther Versions
We address these challenges by developing the first network-aware distributed learning optimization methodology where devices optimally share local data processing and send their learnt parameters to a ...
Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks to each other, with these decisions determined through a convex data transfer optimization ...
The synthetic/real aware learning obtains similar accuracy to federated learning,
parameters comparison is included to provide a more thorough we expect it will improve network resource ...
arXiv:2004.08488v4
fatcat:jjon4swlnfbgzbgaguu6oog3du
Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2102.01375v1 fatcat:igu6csjxe5awrepzj7jmyhno4a
Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. ...
Finally, we discuss the future applications and research directions of federated learning in smart cities. ...
At present, polynomial approximation is used to evaluate nonlinear functions in machine learning algorithms [48] . ...
arXiv:2102.01375v2
fatcat:sxlizo76sjexff3qmj5aueshse
An electronic survey of preferred podcast format and content requirements among trainee emergency medicine specialists in four Southern African universities
2021
African Journal of Emergency Medicine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
learning. ...
Just-in-Time learning proved an unpopular learning strategy in our study population, despite its substantial educational value. ...
Acknowledgements We are grateful to Dr Clyde Matava and colleagues for permission to adapt their survey for use in this study. ...
doi:10.1016/j.afjem.2020.10.014
pmid:33318911
pmcid:PMC7724151
fatcat:wq5ame42rffxrlilx3czm5vwwi
Federated learning and next generation wireless communications: A survey on bidirectional relationship
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Towards this end, a distributed machine learning paradigm termed as Federated learning (FL) has been proposed recently. ...
On one hand, FL plays an important role for optimizing the resources of wireless communication networks, on the other hand, wireless communications is crucial for FL. ...
However, the use of deep learning techniques with a trained neural network will enable true online (real-time) resource allocation and thus solve the NPhard problems efficiently. ...
arXiv:2110.07649v2
fatcat:4grniockzjbbrcl6uqfhsrhhfy
2020 Index IEEE Transactions on Industrial Informatics Vol. 16
2020
IEEE Transactions on Industrial Informatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
for Nonlinear Batch Process Monitoring; TII April 2020 2839-2848 Jiang, S., see Li, Y., 1076-1085 Jiang, X., see Gong, K., 1625-1634 Jiang, X., see Xiao, J., TII April 2020 2177-2188 Jiang, X., see ...
, L., see Cai, H., TII Jan. 2020 587-594 Jiang, L., see Xia, Z., TII Jan. 2020 629-638 Jiang, Q., Yan, S., Yan, X., Yi, H., and Gao, F., Data-Driven Two-Dimensional Deep Correlated Representation Learning ...
., +, TII June 2020 4156-4165 Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics. ...
doi:10.1109/tii.2021.3053362
fatcat:blfvdtsc3fdstnk6qoaazskd3i
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
2019
Proceedings of the IEEE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. ...
Specifically, we first review the background and motivation for AI running at the network edge. ...
[45] advocate a decentralized approach, termed as federated learning, and present FedAvg method for federated learning with the DNN based on iterative model averaging. ...
doi:10.1109/jproc.2019.2918951
fatcat:d53vxmklgfazbmzjhsq3tuoama
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. ...
There is a clear gap in the existing literature in that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. ...
Federated reinforcement learning - [161] , [162] - [163] Federated deep learning --- [41] , [164] Partitioned learning --- [66] FL frameworks. ...
arXiv:2012.01489v1
fatcat:pdauhq4xbbepvf26clhpqnc2ci
2021 Index IEEE Internet of Things Journal Vol. 8
2021
IEEE Internet of Things Journal
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The Author Index contains the primary entry for each item, listed under the first author's name. ...
Hybrid Blockchain-Based Resource Trading System for Federated Learning in Edge Computing. ...
., +, JIoT June 1, 2021 9122-9138 A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things. ...
doi:10.1109/jiot.2022.3141840
fatcat:42a2qzt4jnbwxihxp6rzosha3y
« Previous
Showing results 1 — 15 out of 362 results