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Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression [article]

Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner, Yih-Fang Huang, Anthony Kuh
2021 arXiv   pre-print
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  
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  
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  
., 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  
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

Rentao Gu, Zeyuan Yang, Yuefeng Ji
2020 Journal of Network and Computer Applications  
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  
., 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]

Su Wang, Yichen Ruan, Yuwei Tu, Satyavrat Wagle, Christopher G. Brinton, Carlee Joe-Wong
2021 arXiv   pre-print
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]

Zhaohua Zheng, Yize Zhou, Yilong Sun, Zhang Wang, Boyi Liu, Keqiu Li
2021 arXiv   pre-print
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

K. Ekambaram, H. Lamprecht, V. Lalloo, N. Caruso, A. Engelbrecht, W. Jooste
2021 African Journal of Emergency Medicine  
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]

Debaditya Shome, Omer Waqar, Wali Ullah Khan
2022 arXiv   pre-print
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  
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

Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, Junshan Zhang
2019 Proceedings of the IEEE  
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]

S. Hu, X. Chen, W. Ni, E. Hossain, X. Wang
2020 arXiv   pre-print
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  
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
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