14.1.13.2.1 Federated Learning

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Federated Learning. Federated learning builds upon the server-worker infrastructure, where the workers calculate local gradients and upload them to the server.

Sun, J.[Jun], Chen, T.Y.[Tian-Yi], Giannakis, G.B.[Georgios B.], Yang, Q.M.[Qin-Min], Yang, Z.Y.[Zai-Yue],
Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning,
PAMI(44), No. 4, April 2022, pp. 2031-2044.
IEEE DOI 2203
Quantization (signal), Servers, Technological innovation, Convergence, Frequency modulation, Distributed databases, quantization BibRef

Abdel-Basset, M.[Mohamed], Moustafa, N.[Nour], Hawash, H.[Hossam], Razzak, I.[Imran], Sallam, K.M.[Karam M.], Elkomy, O.M.[Osama M.],
Federated Intrusion Detection in Blockchain-Based Smart Transportation Systems,
ITS(23), No. 3, March 2022, pp. 2523-2537.
IEEE DOI 2203
Security, Blockchains, Intrusion detection, Training, Servers, Feature extraction, Deep learning, Federated learning, blockchain BibRef

Ng, J.S., Lim, W.Y.B., Dai, H.N., Xiong, Z., Huang, J., Niyato, D., Hua, X.S., Leung, C., Miao, C.,
Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles,
ITS(22), No. 4, April 2021, pp. 2326-2344.
IEEE DOI 2104
Training, Computational modeling, Servers, Data models, Unmanned aerial vehicles, Collaborative work, Predictive models, Internet of vehicles BibRef

Yu, Z.X.[Zheng-Xin], Hu, J.[Jia], Min, G.[Geyong], Zhao, Z.W.[Zhi-Wei], Miao, W.[Wang], Hossain, M.S.[M. Shamim],
Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning,
ITS(22), No. 8, August 2021, pp. 5341-5351.
IEEE DOI 2108
Servers, Peer-to-peer computing, Predictive models, Privacy, Machine learning, Security, Probabilistic logic, Content caching, vehicular networks BibRef

Lim, W.Y.B.[Wei Yang Bryan], Huang, J.Q.[Jian-Qiang], Xiong, Z.[Zehui], Kang, J.[Jiawen], Niyato, D.[Dusit], Hua, X.S.[Xian-Sheng], Leung, C.[Cyril], Miao, C.Y.[Chun-Yan],
Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach,
ITS(22), No. 8, August 2021, pp. 5140-5154.
IEEE DOI 2108
Sensors, Computational modeling, Data models, Unmanned aerial vehicles, Contracts, Collaborative work, Training, matching BibRef

Chai, H.[Haoye], Leng, S.[Supeng], Chen, Y.J.[Yi-Jin], Zhang, K.[Ke],
A Hierarchical Blockchain-Enabled Federated Learning Algorithm for Knowledge Sharing in Internet of Vehicles,
ITS(22), No. 7, July 2021, pp. 3975-3986.
IEEE DOI 2107
Blockchain, Collaborative work, Security, Training, Computational modeling, Data models, Servers, knowledge sharing BibRef

Uddin, M.P.[Md Palash], Xiang, Y.[Yong], Yearwood, J.[John], Gao, L.X.[Long-Xiang],
Robust Federated Averaging via Outlier Pruning,
SPLetters(29), 2022, pp. 409-413.
IEEE DOI 2202
Training, Servers, Data models, Arithmetic, Costs, Convergence, Computational modeling, Distributed deep learning, outlier pruning BibRef

Yan, N.[Na], Wang, K.Z.[Ke-Zhi], Pan, C.[Cunhua], Chai, K.K.[Kok Keong],
Performance Analysis for Channel-Weighted Federated Learning in OMA Wireless Networks,
SPLetters(29), 2022, pp. 772-776.
IEEE DOI 2204
Radio frequency, Performance evaluation, Training, Convergence, Distortion, Collaborative work, Wireless sensor networks, orthogonal multiple access BibRef

Ribero, M.[Mónica], Henderson, J.[Jette], Williamson, S.[Sinead], Vikalo, H.[Haris],
Federating recommendations using differentially private prototypes,
PR(129), 2022, pp. 108746.
Elsevier DOI 2206
Recommender systems, Differential Privacy, Federated Learning, Cross-Silo Federated Learning, Matrix Factorization BibRef


Tuor, T.[Tiffany], Wang, S.Q.[Shi-Qiang], Ko, B.J.[Bong Jun], Liu, C.C.[Chang-Chang], Leung, K.K.[Kin K.],
Overcoming Noisy and Irrelevant Data in Federated Learning,
ICPR21(5020-5027)
IEEE DOI 2105
Training, Data privacy, Distributed databases, Machine learning, Benchmark testing, Collaborative work, Data models, Data filtering, open set noise BibRef

Zhang, L.[Lin], Luo, Y.[Yong], Bai, Y.[Yan], Du, B.[Bo], Duan, L.Y.[Ling-Yu],
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment,
ICCV21(4400-4408)
IEEE DOI 2203
Representation learning, Analytical models, Computational modeling, Aggregates, Collaborative work, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Yao, C.H.[Chun-Han], Gong, B.Q.[Bo-Qing], Qi, H.[Hang], Cui, Y.[Yin], Zhu, Y.K.[Yu-Kun], Yang, M.H.[Ming-Hsuan],
Federated Multi-Target Domain Adaptation,
WACV22(1081-1090)
IEEE DOI 2202
Performance evaluation, Training, Image segmentation, Costs, Semantics, Distributed databases, Collaborative work, Transfer, Semi- and Un- supervised Learning BibRef

Li, Q.B.[Qin-Bin], He, B.S.[Bing-Sheng], Song, D.[Dawn],
Model-Contrastive Federated Learning,
CVPR21(10708-10717)
IEEE DOI 2111
Multiple parties to collaboratively train a machine learning model without communicating their local data. Deep learning, Training, Pain, Moon, Object detection, Collaborative work, Data models BibRef

Caldarola, D.[Debora], Mancini, M.[Massimiliano], Galasso, F.[Fabio], Ciccone, M.[Marco], Rodolà, E.[Emanuele], Caputo, B.[Barbara],
Cluster-driven Graph Federated Learning over Multiple Domains,
LLID21(2743-2752)
IEEE DOI 2109
Training, Knowledge engineering, Computational modeling, Benchmark testing, Collaborative work, Data models, Iterative algorithms BibRef

Hao, W.[Weituo], El-Khamy, M.[Mostafa], Lee, J.[Jungwon], Zhang, J.Y.[Jian-Yi], Liang, K.J.[Kevin J], Chen, C.Y.[Chang-You], Carin, L.[Lawrence],
Towards Fair Federated Learning with Zero-Shot Data Augmentation,
TCV21(3305-3314)
IEEE DOI 2109
Computer aided instruction, Distance learning, Distributed databases, Collaborative work, Data models BibRef

Zhu, Z.R.[Zi-Rui], Sun, L.F.[Li-Feng],
Federated Trace: A Node Selection Method for More Efficient Federated Learning,
ICIP21(1234-1238)
IEEE DOI 2201
Training, Measurement, Data privacy, Image processing, Time series analysis, Clustering algorithms, Federated Learning, Communication rounds BibRef

Michieli, U.[Umberto], Ozay, M.[Mete],
Are All Users Treated Fairly in Federated Learning Systems?,
RCV21(2318-2322)
IEEE DOI 2109
Training, Analytical models, Fluctuations, Aggregates, Computational modeling, Training data, Collaborative work BibRef

Lim, J.Q.[Jia Qi], Chan, C.S.[Chee Seng],
From Gradient Leakage To Adversarial Attacks In Federated Learning,
ICIP21(3602-3606)
IEEE DOI 2201
Data privacy, Solid modeling, Computational modeling, Collaborative work, Solids, Data models, Classification algorithms, Adversarial Learning BibRef

Zhu, Z.[Zirui], Sun, L.F.[Li-Feng],
Initialize with Mask: For More Efficient Federated Learning,
MMMod21(II:111-120).
Springer DOI 2106
BibRef

Yao, X., Sun, L.,
Continual Local Training For Better Initialization Of Federated Models,
ICIP20(1736-1740)
IEEE DOI 2011
Training, Data models, Servers, Task analysis, Computational modeling, Distributed databases, Optimization, Generalization BibRef

Moon, J., Kum, S., Kim, Y., Stankovski, V., Pašcinski, U., Kochovski, P.,
A Decentralized AI Data Management System In Federated Learning,
ISCV20(1-4)
IEEE DOI 2011
Big Data, data privacy, learning (artificial intelligence), model training, private locally produced data, Big Data, Machine learning model BibRef

Kathariya, B.[Birendra], Li, L.[Li], Li, Z.[Zhu], Duan, L.Y.[Ling-Yu], Liu, S.[Shan],
Network Update Compression for Federated Learning,
VCIP20(38-41)
IEEE DOI 2102
Servers, Data models, Collaborative work, Uplink, Urban areas, Training, Matrix decomposition, federated learning, Karhunen-Loève Transform (KLT) BibRef

Yao, X., Huang, T., Wu, C., Zhang, R., Sun, L.,
Towards Faster and Better Federated Learning: A Feature Fusion Approach,
ICIP19(175-179)
IEEE DOI 1910
Federated Learning, Feature Fusion, Communication Cost, Generalization BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Fusion for Multiple Classifiers .


Last update:Aug 14, 2022 at 21:20:19