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.Y.[Hao-Ye], 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.[Mˇnica], 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

Hong, S.[Songnam], Chae, J.[Jeongmin],
Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning,
PAMI(44), No. 12, December 2022, pp. 9872-9886.
IEEE DOI 2212
Kernel, Servers, Uplink, Collaborative work, Downlink, Data models, Predictive models, Federated learning, online learning, RKHS BibRef

Ta´k, A.[Afaf], Mlika, Z.[Zoubeir], Cherkaoui, S.[Soumaya],
Clustered Vehicular Federated Learning: Process and Optimization,
ITS(23), No. 12, December 2022, pp. 25371-25383.
IEEE DOI 2212
Data models, Training, Servers, Adaptation models, Computational modeling, Task analysis, Collaborative work, vehicular communication BibRef

Zhu, R.[Rongbo], Li, M.Y.[Meng-Yao], Liu, H.[Hao], Liu, L.[Lu], Ma, M.[Maode],
Federated Deep Reinforcement Learning-Based Spectrum Access Algorithm With Warranty Contract in Intelligent Transportation Systems,
ITS(24), No. 1, January 2023, pp. 1178-1190.
IEEE DOI 2301
Contracts, Warranties, Resource management, Quality of service, Real-time systems, Heuristic algorithms, Vehicle dynamics, quality of service BibRef

Wei, X.X.[Xiao-Xiang], Huang, H.[Hua],
Edge Devices Clustering for Federated Visual Classification: A Feature Norm Based Framework,
IP(32), 2023, pp. 995-1010.
IEEE DOI 2302
Data models, Feature extraction, Computational modeling, Visualization, Training, Federated learning, Adaptation models, clients clustering BibRef

Garin, M.[Marie], Quintana, G.I.[Gonzalo I˝aki],
Incidence of the Sample Size Distribution on One-Shot Federated Learning,
IPOL(13), 2023, pp. 57-64.
DOI Link 2302
BibRef

Sun, T.[Tao], Li, D.S.[Dong-Sheng], Wang, B.[Bao],
Decentralized Federated Averaging,
PAMI(45), No. 4, April 2023, pp. 4289-4301.
IEEE DOI 2303
Servers, Convergence, Costs, Training, Collaborative work, Peer-to-peer computing, Privacy, Decentralized optimization, stochastic gradient descent BibRef


Jain, S.[Shreyansh], Jerripothula, K.R.[Koteswar Rao],
Federated Learning for Commercial Image Sources,
WACV23(6523-6532)
IEEE DOI 2302
Training, Federated learning, Collaboration, Data models, Classification algorithms, Topology, Social good BibRef

Shenaj, D.[Donald], Faný, E.[Eros], Toldo, M.[Marco], Caldarola, D.[Debora], Tavera, A.[Antonio], Michieli, U.[Umberto], Ciccone, M.[Marco], Zanuttigh, P.[Pietro], Caputo, B.[Barbara],
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning,
WACV23(444-454)
IEEE DOI 2302
Training, Adaptation models, Codes, Federated learning, Semantic segmentation, Clustering algorithms BibRef

Quan, P.[Pengrui], Lee, W.H.[Wei-Han], Srivatsa, M.[Mudhakar], Srivastava, M.[Mani],
Enhancing Robustness in Federated Learning by Supervised Anomaly Detection,
ICPR22(996-1003)
IEEE DOI 2212
Federated learning, Distance learning, Computational modeling, Data security, Detectors, Predictive models, Robustness BibRef

Kundalwal, M.K.[Mayank Kumar], Saraswat, A.[Anurag], Mishra, I.[Ishan], Mishra, D.[Deepak],
BAFL: Federated Learning with Base Ablation for Cost Effective Communication,
ICPR22(1922-1928)
IEEE DOI 2212
Costs, Federated learning, Semantics, Neural networks, Distributed databases, Focusing, Feature extraction BibRef

Zaccone, R.[Riccardo], Rizzardi, A.[Andrea], Caldarola, D.[Debora], Ciccone, M.[Marco], Caputo, B.[Barbara],
Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients,
ICPR22(3376-3382)
IEEE DOI 2212
Training, Performance evaluation, Federated learning, Computational modeling, Neural networks, Data models BibRef

Yuan, H.L.[Hao-Lin], Hui, B.[Bo], Yang, Y.C.[Yu-Chen], Burlina, P.[Philippe], Gong, N.Z.Q.[Neil Zhen-Qiang], Cao, Y.[Yinzhi],
Addressing Heterogeneity in Federated Learning via Distributional Transformation,
ECCV22(XXXVIII:179-195).
Springer DOI 2211
BibRef

Varno, F.[Farshid], Saghayi, M.[Marzie], Sevyeri, L.R.[Laya Rafiee], Gupta, S.[Sharut], Matwin, S.[Stan], Havaei, M.[Mohammad],
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation,
ECCV22(XXIII:710-726).
Springer DOI 2211
BibRef

Mugunthan, V.[Vaikkunth], Lin, E.[Eric], Gokul, V.[Vignesh], Lau, C.[Christian], Kagal, L.[Lalana], Pieper, S.[Steve],
FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks,
ECCV22(XII:69-85).
Springer DOI 2211
BibRef

Dong, X.[Xin], Zhang, S.Q.[Sai Qian], Li, A.[Ang], Kung, H.T.,
SphereFed: Hyperspherical Federated Learning,
ECCV22(XXVI:165-184).
Springer DOI 2211
BibRef

Han, S.[Sungwon], Park, S.[Sungwon], Wu, F.Z.[Fang-Zhao], Kim, S.[Sundong], Wu, C.H.[Chu-Han], Xie, X.[Xing], Cha, M.[Meeyoung],
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation,
ECCV22(XXX:691-707).
Springer DOI 2211
BibRef

Caldarola, D.[Debora], Caputo, B.[Barbara], Ciccone, M.[Marco],
Improving Generalization in Federated Learning by Seeking Flat Minima,
ECCV22(XXIII:654-672).
Springer DOI 2211
BibRef

Mendieta, M.[Matias], Yang, T.[Taojiannan], Wang, P.[Pu], Lee, M.W.[Min-Woo], Ding, Z.M.[Zheng-Ming], Chen, C.[Chen],
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning,
CVPR22(8387-8396)
IEEE DOI 2210
Performance evaluation, Computer aided instruction, Systematics, Federated learning, Distance learning, Data models, Efficient learning and inferences BibRef

Fang, X.W.[Xiu-Wen], Ye, M.[Mang],
Robust Federated Learning with Noisy and Heterogeneous Clients,
CVPR22(10062-10071)
IEEE DOI 2210
Adaptation models, Privacy, Computational modeling, Collaboration, Collaborative work, Data models, Privacy and federated learning BibRef

Li, X.C.[Xin-Chun], Xu, Y.C.[Yi-Chu], Song, S.M.[Shao-Ming], Li, B.S.[Bing-Shuai], Li, Y.C.[Yin-Chuan], Shao, Y.F.[Yun-Feng], Zhan, D.C.[De-Chuan],
Federated Learning with Position-Aware Neurons,
CVPR22(10072-10081)
IEEE DOI 2210
Training, Fuses, Neurons, Optimization methods, Collaborative work, Encoding, Privacy and federated learning, Representation learning BibRef

Ma, X.S.[Xiao-Song], Zhang, J.[Jie], Guo, S.[Song], Xu, W.C.[Wen-Chao],
Layer-wised Model Aggregation for Personalized Federated Learning,
CVPR22(10082-10091)
IEEE DOI 2210
Training, Distributed databases, Collaboration, Collaborative work, Data models, Pattern recognition, Privacy and federated learning, Others BibRef

Tang, M.[Minxue], Ning, X.F.[Xue-Fei], Wang, Y.[Yitu], Sun, J.W.[Jing-Wei], Wang, Y.[Yu], Li, H.[Hai], Chen, Y.[Yiran],
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning,
CVPR22(10092-10101)
IEEE DOI 2210
Training, Correlation, Federated learning, Gaussian processes, Pattern recognition, Convergence, Privacy and federated learning, Machine learning BibRef

Gao, L.[Liang], Fu, H.Z.[Hua-Zhu], Li, L.[Li], Chen, Y.[Yingwen], Xu, M.[Ming], Xu, C.Z.[Cheng-Zhong],
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction,
CVPR22(10102-10111)
IEEE DOI 2210
Training, Federated learning, Heuristic algorithms, Computational modeling, Data models, Pattern recognition, Privacy and federated learning BibRef

Cheng, A.[Anda], Wang, P.S.[Pei-Song], Zhang, X.S.[Xi Sheryl], Cheng, J.[Jian],
Differentially Private Federated Learning with Local Regularization and Sparsification,
CVPR22(10112-10121)
IEEE DOI 2210
Degradation, Privacy, Differential privacy, Costs, Federated learning, Computational modeling, privacy and ethics in vision BibRef

Li, Z.[Zhuohang], Zhang, J.X.[Jia-Xin], Liu, L.Y.[Lu-Yang], Liu, J.[Jian],
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage,
CVPR22(10122-10132)
IEEE DOI 2210
Degradation, Privacy, Data privacy, Perturbation methods, Training data, Generative adversarial networks, Image and video synthesis and generation BibRef

Huang, W.K.[Wen-Ke], Ye, M.[Mang], Du, B.[Bo],
Learn from Others and Be Yourself in Heterogeneous Federated Learning,
CVPR22(10133-10143)
IEEE DOI 2210
Degradation, Privacy, Distance learning, Distributed databases, Collaboration, Collaborative work, Data models, Privacy and federated learning BibRef

Liang, X.X.[Xiao-Xiao], Lin, Y.Q.[Yi-Qun], Fu, H.Z.[Hua-Zhu], Zhu, L.[Lei], Li, X.M.[Xiao-Meng],
RSCFed: Random Sampling Consensus Federated Semi-supervised Learning,
CVPR22(10144-10153)
IEEE DOI 2210
Training, Computational modeling, Distributed databases, Semisupervised learning, Benchmark testing, Collaborative work, Self- semi- meta- unsupervised learning BibRef

Xu, J.Y.[Jing-Yi], Chen, Z.[Zihan], Quek, T.Q.S.[Tony Q.S.], Chong, K.F.E.[Kai Fong Ernest],
FedCorr: Multi-Stage Federated Learning for Label Noise Correction,
CVPR22(10174-10183)
IEEE DOI 2210
Training, Data privacy, Adaptation models, Collaborative work, Loss measurement, Data models, Stability analysis, Machine learning BibRef

Zhang, J.Y.[Jing-Yang], Chen, Y.R.[Yi-Ran], Li, H.[Hai],
Privacy Leakage of Adversarial Training Models in Federated Learning Systems,
ArtOfRobust22(107-113)
IEEE DOI 2210
Training, Deep learning, Privacy, Neural networks, Collaborative work BibRef

Becking, D.[Daniel], Kirchhoffer, H.[Heiner], Tech, G.[Gerhard], Haase, P.[Paul], MŘller, K.[Karsten], Schwarz, H.[Heiko], Samek, W.[Wojciech],
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning,
FedVision22(3366-3375)
IEEE DOI 2210
Adaptation models, Computational modeling, Pipelines, Neural networks, Collaborative work, Data models BibRef

Cheng, G.[Gary], Charles, Z.[Zachary], Garrett, Z.[Zachary], Rush, K.[Keith],
Does Federated Dropout actually work?,
FedVision22(3386-3394)
IEEE DOI 2210
Training, Measurement, Machine learning algorithms, Computational modeling, Memory management BibRef

Qu, L.Q.[Liang-Qiong], Zhou, Y.Y.[Yu-Yin], Liang, P.P.[Paul Pu], Xia, Y.D.[Ying-Da], Wang, F.F.[Fei-Fei], Adeli, E.[Ehsan], Fei-Fei, L.[Li], Rubin, D.[Daniel],
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning,
CVPR22(10051-10061)
IEEE DOI 2210
Training, Federated learning, Organizations, Transformers, Data models, Robustness, Privacy and federated learning 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], RodolÓ, 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., PaÜcinski, 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-LoŔve 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:Mar 27, 2023 at 09:32:08