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.[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
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
Taïk, 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
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 Iñaki],
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
Zhou, H.L.[Hong-Liang],
Zheng, Y.F.[Yi-Feng],
Huang, H.J.[He-Jiao],
Shu, J.G.[Jian-Gang],
Jia, X.H.[Xiao-Hua],
Toward Robust Hierarchical Federated Learning in Internet of Vehicles,
ITS(24), No. 5, May 2023, pp. 5600-5614.
IEEE DOI
2305
Federated learning, Training, Servers, Robustness,
Internet of Vehicles, Convergence, Computational modeling,
robustness
BibRef
Ahmad, A.[Adnan],
Luo, W.[Wei],
Robles-Kelly, A.[Antonio],
Robust federated learning under statistical heterogeneity via Hessian
spectral decomposition,
PR(141), 2023, pp. 109635.
Elsevier DOI
2306
Federated learning, Hessian, Non-IID data
BibRef
Hatamizadeh, A.[Ali],
Yin, H.X.[Hong-Xu],
Molchanov, P.[Pavlo],
Myronenko, A.[Andriy],
Li, W.Q.[Wen-Qi],
Dogra, P.[Prerna],
Feng, A.[Andrew],
Flores, M.G.[Mona G],
Kautz, J.[Jan],
Xu, D.[Daguang],
Roth, H.R.[Holger R.],
Do Gradient Inversion Attacks Make Federated Learning Unsafe?,
MedImg(42), No. 7, July 2023, pp. 2044-2056.
IEEE DOI
2307
Training, Servers, Data models, Computational modeling,
Medical services, Image reconstruction, Artificial intelligence, security
BibRef
Zhou, S.L.[Sheng-Long],
Li, G.Y.[Geoffrey Ye],
Federated Learning Via Inexact ADMM,
PAMI(45), No. 8, August 2023, pp. 9699-9708.
IEEE DOI
2307
Servers, Convergence, Optimization, Approximation algorithms, Training,
Federated learning, Convex functions, partial device participation
BibRef
Dong, N.Q.[Nan-Qing],
Kampffmeyer, M.[Michael],
Voiculescu, I.[Irina],
Xing, E.[Eric],
Federated Partially Supervised Learning With Limited Decentralized
Medical Images,
MedImg(42), No. 7, July 2023, pp. 1944-1954.
IEEE DOI
2307
Task analysis, Feature extraction, Supervised learning,
Biomedical imaging, Data models, Servers, Training,
multi-label classification
BibRef
Shi, Y.[Yong],
Zhang, Y.Y.[Yuan-Ying],
Zhang, P.[Peng],
Xiao, Y.[Yang],
Niu, L.F.[Ling-Feng],
Federated learning with l1 regularization,
PRL(172), 2023, pp. 15-21.
Elsevier DOI
2309
Federated learning, Regularization, Stochastic subgradient descent
BibRef
Jin, X.[Xiating],
Bu, J.J.[Jia-Jun],
Yu, Z.[Zhi],
Zhang, H.[Hui],
Wang, Y.[Yaonan],
FedCrack: Federated Transfer Learning With Unsupervised
Representation for Crack Detection,
ITS(24), No. 10, October 2023, pp. 11171-11184.
IEEE DOI
2310
BibRef
Shinde, S.S.[Swapnil Sadashiv],
Tarchi, D.[Daniele],
Joint Air-Ground Distributed Federated Learning for Intelligent
Transportation Systems,
ITS(24), No. 9, September 2023, pp. 9996-10011.
IEEE DOI
2310
BibRef
Novoa-Paradela, D.[David],
Fontenla-Romero, O.[Oscar],
Guijarro-Berdiñas, B.[Bertha],
Fast deep autoencoder for federated learning,
PR(143), 2023, pp. 109805.
Elsevier DOI
2310
Deep autoencoder, Anomaly detection, Federated learning,
Edge computing, Machine learning
BibRef
Liu, Z.B.[Zhen-Bing],
Wu, F.F.[Feng-Feng],
Wang, Y.M.[Yu-Meng],
Yang, M.Y.[Meng-Yu],
Pan, X.P.[Xi-Peng],
FedCL: Federated contrastive learning for multi-center medical image
classification,
PR(143), 2023, pp. 109739.
Elsevier DOI
2310
Federated learning, Contrastive learning, Image classification
BibRef
Sheng, T.[Tao],
Shen, C.C.[Cheng-Chao],
Liu, Y.[Yuan],
Ou, Y.[Yeyu],
Qu, Z.[Zhe],
Liang, Y.X.[Yi-Xiong],
Wang, J.X.[Jian-Xin],
Modeling global distribution for federated learning with label
distribution skew,
PR(143), 2023, pp. 109724.
Elsevier DOI
2310
Federated learning, Label distribution skew,
Generative adversarial network, Non-Independent and identically distributed
BibRef
Ma, B.T.[Ben-Teng],
Feng, Y.[Yu],
Chen, G.[Geng],
Li, C.Y.[Chang-Yang],
Xia, Y.[Yong],
Federated adaptive reweighting for medical image classification,
PR(144), 2023, pp. 109880.
Elsevier DOI
2310
Medical image classification, Federated learning, Deep learning
BibRef
Zhang, Z.[Zheng],
Ma, X.[Xindi],
Ma, J.F.[Jian-Feng],
Local Differential Privacy Based Membership-Privacy-Preserving
Federated Learning for Deep-Learning-Driven Remote Sensing,
RS(15), No. 20, 2023, pp. 5050.
DOI Link
2310
BibRef
Kumar, R.[Ramakant],
Mishra, R.[Rahul],
Gupta, H.P.[Hari Prabhat],
A Federated Learning Approach With Imperfect Labels in LoRa-Based
Transportation Systems,
ITS(24), No. 11, November 2023, pp. 13099-13107.
IEEE DOI
2311
BibRef
Kwon, D.[Dohyeok],
Park, J.[Jonghwan],
Hong, S.[Songnam],
Tighter Regret Analysis and Optimization of Online Federated Learning,
PAMI(45), No. 12, December 2023, pp. 15772-15789.
IEEE DOI
2311
BibRef
Sun, Y.[Yan],
Shen, L.[Li],
Sun, H.[Hao],
Ding, L.[Liang],
Tao, D.C.[Da-Cheng],
Efficient Federated Learning Via Local Adaptive Amended Optimizer
With Linear Speedup,
PAMI(45), No. 12, December 2023, pp. 14453-14464.
IEEE DOI
2311
BibRef
Phong, L.T.[Le Trieu],
Phuong, T.T.[Tran Thi],
Wang, L.H.[Li-Hua],
Ozawa, S.[Seiichi],
Frameworks for Privacy-Preserving Federated Learning,
IEICE(E107-D), No. 1, January 2024, pp. 2-12.
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Shaik, T.[Thanveer],
Tao, X.H.[Xiao-Hui],
Li, L.[Lin],
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Zhou, X.[Xujuan],
Yong, J.M.[Jian-Ming],
Clustered FedStack: Intermediate Global Models with Bayesian
Information Criterion,
PRL(177), 2024, pp. 121-127.
Elsevier DOI
2401
Federated learning, FedStack, Clustering, Bayesian, Cyclical learning rates
BibRef
Li, Y.[Yanan],
Yang, S.[Shusen],
Ren, X.B.[Xue-Bin],
Shi, L.[Liang],
Zhao, C.[Cong],
Multi-Stage Asynchronous Federated Learning With Adaptive
Differential Privacy,
PAMI(46), No. 2, February 2024, pp. 1243-1256.
IEEE DOI
2401
BibRef
Huang, W.K.[Wen-Ke],
Ye, M.[Mang],
Shi, Z.K.[Ze-Kun],
Du, B.[Bo],
Generalizable Heterogeneous Federated Cross-Correlation and Instance
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PAMI(46), No. 2, February 2024, pp. 712-728.
IEEE DOI Code:
WWW Link.
2401
Heterogeneous federated learning, catastrophic forgetting,
self-supervised learning, knowledge distillation
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Zhao, Z.N.[Zhong-Nan],
Liang, X.L.[Xiao-Liang],
Huang, H.[Hai],
Wang, K.[Kun],
Deep federated learning hybrid optimization model based on encrypted
aligned data,
PR(148), 2024, pp. 110193.
Elsevier DOI
2402
Federated learning, Gaussian Mixture Model,
Variational AutoEncoder, Encrypted aligned data, Privacy protection
BibRef
Ribero, M.[Mónica],
Vikalo, H.[Haris],
Reducing communication in federated learning via efficient client
sampling,
PR(148), 2024, pp. 110122.
Elsevier DOI
2402
Federated learning, Machine learning, Distributed optimization
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Wang, W.D.[Wei-Dong],
Li, S.Q.[Si-Qi],
Zhang, J.[Jihao],
Shan, D.[Dan],
Zhang, G.[Guangwei],
Gao, X.[Xiang],
A Node Selection Strategy in Space-Air-Ground Information Networks:
A Double Deep Q-Network Based on the Federated Learning Training Method,
RS(16), No. 4, 2024, pp. 651.
DOI Link
2402
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Wang, S.F.[Shan-Feng],
Tao, H.[Hao],
Li, J.Z.[Jian-Zhao],
Ji, X.Y.[Xin-Yuan],
Gao, Y.[Yuan],
Gong, M.[Maoguo],
Towards fair and personalized federated recommendation,
PR(149), 2024, pp. 110234.
Elsevier DOI
2403
Federated learning, Fairness, Graph neural network, Personalized recommendation
BibRef
Kang, M.[Myeongkyun],
Kim, S.[Soopil],
Jin, K.H.[Kyong Hwan],
Adeli, E.[Ehsan],
Pohl, K.M.[Kilian M.],
Park, S.H.[Sang Hyun],
FedNN: Federated learning on concept drift data using weight and
adaptive group normalizations,
PR(149), 2024, pp. 110230.
Elsevier DOI
2403
Federated learning, Concept drift, Weight normalization,
Adaptive group normalization
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Dong, J.H.[Jia-Hua],
Li, H.L.[Hong-Liu],
Cong, Y.[Yang],
Sun, G.[Gan],
Zhang, Y.[Yulun],
Van Gool, L.J.[Luc J.],
No One Left Behind: Real-World Federated Class-Incremental Learning,
PAMI(46), No. 4, April 2024, pp. 2054-2070.
IEEE DOI
2403
Task analysis, Training, Prototypes, COVID-19, Servers, Semantics,
Privacy, Catastrophic forgetting, class imbalance,
privacy preservation
BibRef
Wu, Z.[Zheshun],
Xu, Z.L.[Zeng-Lin],
Yu, H.F.[Hong-Fang],
Liu, J.[Jie],
Information-Theoretic Generalization Analysis for Topology-Aware
Heterogeneous Federated Edge Learning Over Noisy Channels,
SPLetters(31), 2024, pp. 691-695.
IEEE DOI
2403
Computational modeling, Topology, Analytical models, Data models,
Noise measurement, Federated learning, noisy channels
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Kumar, K.N.[Kummari Naveen],
Mohan, C.K.[Chalavadi Krishna],
Cenkeramaddi, L.R.[Linga Reddy],
The Impact of Adversarial Attacks on Federated Learning: A Survey,
PAMI(46), No. 5, May 2024, pp. 2672-2691.
IEEE DOI
2404
Survey, Federated Learning. Surveys, Data models, Security, Data privacy, Servers,
Transfer learning, Training, Adversarial attacks, visibility
BibRef
Shi, Y.J.[Yu-Jun],
Liang, J.[Jian],
Zhang, W.Q.[Wen-Qing],
Xue, C.H.[Chu-Hui],
Tan, V.Y.F.[Vincent Y. F.],
Bai, S.[Song],
Understanding and Mitigating Dimensional Collapse in Federated
Learning,
PAMI(46), No. 5, May 2024, pp. 2936-2949.
IEEE DOI
2404
Data models, Federated learning, Training, Computational modeling,
Analytical models, Decorrelation, Self-supervised learning, dimensional collapse
BibRef
Guan, H.[Hao],
Yap, P.T.[Pew-Thian],
Bozoki, A.[Andrea],
Liu, M.X.[Ming-Xia],
Federated learning for medical image analysis: A survey,
PR(151), 2024, pp. 110424.
Elsevier DOI
2404
Survey, Federated Learning. Federated learning, Machine learning, Medical image analysis, Data privacy
BibRef
Wan, G.[Guangnian],
Du, H.T.[Hai-Tao],
Yuan, X.J.[Xue-Jing],
Yang, J.[Jun],
Chen, M.L.[Mei-Ling],
Xu, J.[Jie],
Enhancing Privacy Preservation in Federated Learning via Learning
Rate Perturbation,
ICCV23(4749-4758)
IEEE DOI
2401
BibRef
Zeng, Y.[Yaopei],
Liu, L.[Lei],
Liu, L.[Li],
Shen, L.[Li],
Liu, S.[Shaoguo],
Wu, B.Y.[Bao-Yuan],
Global Balanced Experts for Federated Long-Tailed Learning,
ICCV23(4792-4802)
IEEE DOI
2401
BibRef
Chen, H.[Haokun],
Frikha, A.[Ahmed],
Krompass, D.[Denis],
Gu, J.D.[Jin-Dong],
Tresp, V.[Volker],
FRAug: Tackling Federated Learning with Non-IID Features via
Representation Augmentation,
ICCV23(4826-4836)
IEEE DOI
2401
BibRef
Ghodsi, Z.[Zahra],
Javaheripi, M.[Mojan],
Sheybani, N.[Nojan],
Zhang, X.[Xinqiao],
Huang, K.[Ke],
Koushanfar, F.[Farinaz],
zPROBE: Zero Peek Robustness Checks for Federated Learning,
ICCV23(4837-4847)
IEEE DOI
2401
BibRef
Yang, C.[Chen],
Zhu, M.[Meilu],
Liu, Y.F.[Yi-Fan],
Yuan, Y.X.[Yi-Xuan],
FedPD: Federated Open Set Recognition with Parameter Disentanglement,
ICCV23(4859-4868)
IEEE DOI
2401
BibRef
Sun, G.Y.[Guang-Yu],
Mendieta, M.[Matias],
Luo, J.[Jun],
Wu, S.D.[Shan-Dong],
Chen, C.[Chen],
FedPerfix: Towards Partial Model Personalization of Vision
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ICCV23(4965-4975)
IEEE DOI Code:
WWW Link.
2401
BibRef
Han, S.[Sungwon],
Park, S.[Sungwon],
Wu, F.Z.[Fang-Zhao],
Kim, S.[Sundong],
Zhu, B.[Bin],
Xie, X.[Xing],
Cha, M.[Meeyoung],
Towards Attack-tolerant Federated Learning via Critical Parameter
Analysis,
ICCV23(4976-4985)
IEEE DOI
2401
BibRef
Fang, X.W.[Xiu-Wen],
Ye, M.[Mang],
Yang, X.[Xiyuan],
Robust Heterogeneous Federated Learning under Data Corruption,
ICCV23(4997-5007)
IEEE DOI
2401
BibRef
Zhou, Y.H.[Yu-Hao],
Shi, M.J.[Ming-Jia],
Li, Y.Y.X.[Yuan-Yan-Xi],
Sun, Y.[Yanan],
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Communication-efficient Federated Learning with Single-Step Synthetic
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ICCV23(5008-5017)
IEEE DOI
2401
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Zhang, J.Q.[Jian-Qing],
Hua, Y.[Yang],
Wang, H.[Hao],
Song, T.[Tao],
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GPFL: Simultaneously Learning Global and Personalized Feature
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ICCV23(5018-5028)
IEEE DOI
2401
BibRef
Vahidian, S.[Saeed],
Kadaveru, S.[Sreevatsank],
Baek, W.[Woonjoon],
Wang, W.J.[Wei-Jia],
Kungurtsev, V.[Vyacheslav],
Chen, C.[Chen],
Shah, M.[Mubarak],
Lin, B.[Bill],
When Do Curricula Work in Federated Learning?,
ICCV23(5061-5071)
IEEE DOI
2401
BibRef
Zhang, C.[Chi],
Zhang, X.M.[Xiao-Man],
Sotthiwat, E.[Ekanut],
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ICCV23(5103-5112)
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2401
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Sun, J.W.[Jing-Wei],
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Xu, D.[Daguang],
Chen, Y.[Yiran],
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Communication-Efficient Vertical Federated Learning with Limited
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ICCV23(5180-5189)
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2401
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Kim, H.[Hansol],
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ProtoFL: Unsupervised Federated Learning via Prototypical
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ICCV23(6447-6456)
IEEE DOI
2401
BibRef
Ur Rehman, Y.A.[Yasar Abbas],
Gao, Y.[Yan],
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Shen, J.J.[Jia-Jun],
Lane, N.D.[Nicholas D.],
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated
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ICCV23(16418-16427)
IEEE DOI
2401
BibRef
Chen, R.[Rui],
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Workie-Talkie: Accelerating Federated Learning by Overlapping
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ICCV23(16953-16963)
IEEE DOI
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Cho, Y.J.[Yae Jee],
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Local or Global: Selective Knowledge Assimilation for Federated
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ICCV23(17041-17050)
IEEE DOI
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Yang, F.E.[Fu-En],
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ICCV23(19102-19111)
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Personalized Semantics Excitation for Federated Image Classification,
ICCV23(19244-19253)
IEEE DOI
2401
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Wu, X.H.[Xing-Hao],
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ICCV23(19318-19327)
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ICCV23(19328-19339)
IEEE DOI
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Do, T.[Tuong],
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Towards Instance-adaptive Inference for Federated Learning,
ICCV23(23230-23239)
IEEE DOI
2401
BibRef
Zhuang, W.M.[Wei-Ming],
Wen, Y.G.[Yong-Gang],
Lyu, L.J.[Ling-Juan],
Zhang, S.[Shuai],
MAS: Towards Resource-Efficient Federated Multiple-Task Learning,
ICCV23(23357-23367)
IEEE DOI
2401
BibRef
Psaltis, A.[Athanasios],
Kastellos, A.[Anestis],
Patrikakis, C.Z.[Charalampos Z.],
Daras, P.[Petros],
FedLID: Self-Supervised Federated Learning for Leveraging Limited
Image Data,
LIMIT23(1031-1040)
IEEE DOI
2401
BibRef
Caldarola, D.[Debora],
Caputo, B.[Barbara],
Ciccone, M.[Marco],
Window-based Model Averaging Improves Generalization in Heterogeneous
Federated Learning,
WiCV-ICCV23(2255-2263)
IEEE DOI
2401
BibRef
Pennisi, M.[Matteo],
Salanitri, F.P.[Federica Proietto],
Bellitto, G.[Giovanni],
Spampinato, C.[Concetto],
Palazzo, S.[Simone],
Casella, B.[Bruno],
Aldinucci, M.[Marco],
Experience Replay as an Effective Strategy for Optimizing
Decentralized Federated Learning,
VCL23(3368-3375)
IEEE DOI
2401
BibRef
Luo, J.[Jun],
Mendieta, M.[Matias],
Chen, C.[Chen],
Wu, S.D.[Shan-Dong],
PGFed: Personalize Each Client's Global Objective for Federated
Learning,
ICCV23(3923-3933)
IEEE DOI Code:
WWW Link.
2401
BibRef
Liu, L.X.[Liang-Xi],
Jiang, X.[Xi],
Zheng, F.[Feng],
Chen, H.[Hong],
Qi, G.J.[Guo-Jun],
Huang, H.[Heng],
Shao, L.[Ling],
A Bayesian Federated Learning Framework With Online Laplace
Approximation,
PAMI(46), No. 1, January 2024, pp. 1-16.
IEEE DOI
2312
BibRef
Pennisi, M.[Matteo],
Salanitri, F.P.[Federica Proietto],
Bellitto, G.[Giovanni],
Casella, B.[Bruno],
Aldinucci, M.[Marco],
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Spampinato, C.[Concetto],
FedER: Federated Learning through Experience Replay and
privacy-preserving data synthesis,
CVIU(238), 2024, pp. 103882.
Elsevier DOI Code:
WWW Link.
2312
Decentralized learning, Federated learning,
Privacy in machine learning, Pattern recognition and classification
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Su, R.Z.[Rui-Zheng],
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IET-CV(17), No. 8, 2023, pp. 913-924.
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2312
computer vision, image classification
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Li, Y.[Yue],
MDFD: Study of Distributed Non-IID Scenarios and Frechet
Distance-Based Evaluation,
ICIP23(2300-2304)
IEEE DOI
2312
BibRef
Qian, P.X.[Pin-Xin],
Lu, Y.[Yang],
Wang, H.Z.[Han-Zi],
Long-Tailed Federated Learning Via Aggregated Meta Mapping,
ICIP23(2010-2014)
IEEE DOI
2312
BibRef
Lee, Y.J.[Young-Joon],
Park, S.[Sangwoo],
Kang, J.[Joonhyuk],
Fast-Convergent Federated Learning via Cyclic Aggregation,
ICIP23(2175-2179)
IEEE DOI
2312
BibRef
Gao, T.R.[Tian-Run],
Liu, X.H.[Xiao-Hong],
Yang, Y.N.[Yu-Ning],
Wang, G.Y.[Guang-Yu],
FEDMBP: Multi-Branch Prototype Federated Learning on Heterogeneous
Data,
ICIP23(2180-2184)
IEEE DOI
2312
BibRef
Pi, R.J.[Ren-Jie],
Zhang, W.Z.[Wei-Zhong],
Xie, Y.Q.[Yue-Qi],
Gao, J.H.[Jia-Hui],
Wang, X.Y.[Xiao-Yu],
Kim, S.[Sunghun],
Chen, Q.F.[Qi-Feng],
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics,
CVPR23(12177-12186)
IEEE DOI
2309
BibRef
Zhang, T.[Tuo],
Gao, L.[Lei],
Lee, S.[Sunwoo],
Zhang, M.[Mi],
Avestimehr, S.[Salman],
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with
Adaptive Partial Training,
FedVision23(5064-5073)
IEEE DOI
2309
BibRef
Ovi, P.R.[Pretom Roy],
Dey, E.[Emon],
Roy, N.[Nirmalya],
Gangopadhyay, A.[Aryya],
Mixed Quantization Enabled Federated Learning to Tackle Gradient
Inversion Attacks,
FedVision23(5046-5054)
IEEE DOI
2309
BibRef
Cai, R.[Ruisi],
Chen, X.H.[Xiao-Han],
Liu, S.W.[Shi-Wei],
Srinivasa, J.[Jayanth],
Lee, M.[Myungjin],
Kompella, R.[Ramana],
Wang, Z.Y.[Zhang-Yang],
Many-Task Federated Learning: A New Problem Setting and A Simple
Baseline,
FedVision23(5037-5045)
IEEE DOI
2309
BibRef
Chen, D.S.[Deng-Sheng],
Tan, V.J.[Vince Junkai],
Lu, Z.L.[Zhi-Lin],
Wu, E.[Enhua],
Hu, J.[Jie],
OpenFed: A Comprehensive and Versatile Open-Source Federated Learning
Framework,
FedVision23(5018-5026)
IEEE DOI
2309
BibRef
Chen, H.[Huancheng],
Vikalo, H.[Haris],
Federated Learning in Non-IID Settings Aided by Differentially
Private Synthetic Data,
FedVision23(5027-5036)
IEEE DOI
2309
BibRef
Shi, Y.F.[Yi-Fan],
Liu, Y.Q.[Ying-Qi],
Wei, K.[Kang],
Shen, L.[Li],
Wang, X.Q.[Xue-Qian],
Tao, D.C.[Da-Cheng],
Make Landscape Flatter in Differentially Private Federated Learning,
CVPR23(24552-24562)
IEEE DOI
2309
BibRef
Zhu, J.[Junyi],
Ma, X.C.[Xing-Chen],
Blaschko, M.B.[Matthew B.],
Confidence-Aware Personalized Federated Learning via Variational
Expectation Maximization,
CVPR23(24542-24551)
IEEE DOI
2309
BibRef
Ilhan, F.[Fatih],
Su, G.[Gong],
Liu, L.[Ling],
ScaleFL: Resource-Adaptive Federated Learning with Heterogeneous
Clients,
CVPR23(24532-24541)
IEEE DOI
2309
BibRef
Liao, D.P.[Dong-Ping],
Gao, X.[Xitong],
Zhao, Y.[Yiren],
Xu, C.Z.[Cheng-Zhong],
Adaptive Channel Sparsity for Federated Learning under System
Heterogeneity,
CVPR23(20432-20441)
IEEE DOI
2309
BibRef
Xu, Y.Y.[Yuan-Yi],
Lin, C.S.[Ci-Siang],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Bias-Eliminating Augmentation Learning for Debiased Federated
Learning,
CVPR23(20442-20452)
IEEE DOI
2309
BibRef
Wang, H.Z.[Hao-Zhao],
Li, Y.C.[Yi-Chen],
Xu, W.C.[Wen-Chao],
Li, R.X.[Rui-Xuan],
Zhan, Y.F.[Yu-Feng],
Zeng, Z.G.[Zhi-Gang],
DaFKD: Domain-aware Federated Knowledge Distillation,
CVPR23(20412-20421)
IEEE DOI
2309
BibRef
Qin, Z.X.[Zi-Xuan],
Yang, L.[Liu],
Wang, Q.L.[Qi-Long],
Han, Y.[Yahong],
Hu, Q.H.[Qing-Hua],
Reliable and Interpretable Personalized Federated Learning,
CVPR23(20422-20431)
IEEE DOI
2309
BibRef
Chow, K.H.[Ka-Ho],
Liu, L.[Ling],
Wei, W.Q.[Wen-Qi],
Ilhan, F.[Fatih],
Wu, Y.Z.[Yan-Zhao],
STDLens: Model Hijacking-Resilient Federated Learning for Object
Detection,
CVPR23(16343-16351)
IEEE DOI
2309
BibRef
Xiong, Y.H.[Yuan-Hao],
Wang, R.[Ruochen],
Cheng, M.[Minhao],
Yu, F.[Felix],
Hsieh, C.J.[Cho-Jui],
FedDM: Iterative Distribution Matching for Communication-Efficient
Federated Learning,
CVPR23(16323-16332)
IEEE DOI
2309
BibRef
Huang, W.K.[Wen-Ke],
Ye, M.[Mang],
Shi, Z.K.[Ze-Kun],
Li, H.[He],
Du, B.[Bo],
Rethinking Federated Learning with Domain Shift: A Prototype View,
CVPR23(16312-16322)
IEEE DOI
2309
BibRef
Li, M.[Ming],
Li, Q.L.[Qing-Li],
Wang, Y.[Yan],
Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised
Learning,
CVPR23(16292-16301)
IEEE DOI
2309
BibRef
Chen, D.S.[Deng-Sheng],
Hu, J.[Jie],
Tan, V.J.[Vince Junkai],
Wei, X.M.[Xiao-Ming],
Wu, E.[Enhua],
Elastic Aggregation for Federated Optimization,
CVPR23(12187-12197)
IEEE DOI
2309
BibRef
Qu, Z.[Zhe],
Li, X.Y.[Xing-Yu],
Han, X.[Xiao],
Duan, R.[Rui],
Shen, C.C.[Cheng-Chao],
Chen, L.X.[Li-Xing],
How to Prevent the Poor Performance Clients for Personalized
Federated Learning?,
CVPR23(12167-12176)
IEEE DOI
2309
BibRef
Feng, C.M.[Chun-Mei],
Li, B.[Bangjun],
Xu, X.X.[Xin-Xing],
Liu, Y.[Yong],
Fu, H.Z.[Hua-Zhu],
Zuo, W.M.[Wang-Meng],
Learning Federated Visual Prompt in Null Space for MRI Reconstruction,
CVPR23(8064-8073)
IEEE DOI
2309
BibRef
Duan, J.H.[Jian-Hui],
Li, W.Z.[Wen-Zhong],
Zou, D.[Derun],
Li, R.[Ruichen],
Lu, S.[Sanglu],
Federated Learning with Data-Agnostic Distribution Fusion,
CVPR23(8074-8083)
IEEE DOI
2309
BibRef
Miao, J.X.[Jia-Xu],
Yang, Z.X.[Zong-Xin],
Fan, L.L.[Lei-Lei],
Yang, Y.[Yi],
FedSeg: Class-Heterogeneous Federated Learning for Semantic
Segmentation,
CVPR23(8042-8052)
IEEE DOI
2309
BibRef
Zhao, J.C.[Joshua C.],
Elkordy, A.R.[Ahmed Roushdy],
Sharma, A.[Atul],
Ezzeldin, Y.H.[Yahya H.],
Avestimehr, S.[Salman],
Bagchi, S.[Saurabh],
The Resource Problem of Using Linear Layer Leakage Attack in
Federated Learning,
CVPR23(3974-3983)
IEEE DOI
2309
BibRef
Li, B.[Bo],
Schmidt, M.N.[Mikkel N.],
Alstrøm, T.S.[Tommy S.],
Stich, S.U.[Sebastian U.],
On the Effectiveness of Partial Variance Reduction in Federated
Learning with Heterogeneous Data,
CVPR23(3964-3973)
IEEE DOI
2309
BibRef
Zhang, R.P.[Rui-Peng],
Xu, Q.[Qinwei],
Yao, J.C.[Jiang-Chao],
Zhang, Y.[Ya],
Tian, Q.[Qi],
Wang, Y.F.[Yan-Feng],
Federated Domain Generalization with Generalization Adjustment,
CVPR23(3954-3963)
IEEE DOI
2309
BibRef
Luo, K.Y.[Kang-Yang],
Li, X.[Xiang],
Lan, Y.S.[Yun-Shi],
Gao, M.[Ming],
GradMA: A Gradient-Memory-based Accelerated Federated Learning with
Alleviated Catastrophic Forgetting,
CVPR23(3708-3717)
IEEE DOI
2309
BibRef
Li, Y.L.[Yan-Li],
Sani, A.S.[Abubakar Sadiq],
Yuan, D.[Dong],
Bao, W.[Wei],
Enhancing Federated Learning Robustness Through Clustering Non-iid
Features,
ACCVWS22(45-59).
Springer DOI
2307
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],
Fanì, 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],
Müller, 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
Yan, R.[Rui],
Qu, L.Q.[Liang-Qiong],
Wei, Q.Y.[Qing-Yue],
Huang, S.C.[Shih-Cheng],
Shen, L.[Liyue],
Rubin, D.L.[Daniel L.],
Xing, L.[Lei],
Zhou, Y.[Yuyin],
Label-Efficient Self-Supervised Federated Learning for Tackling Data
Heterogeneity in Medical Imaging,
MedImg(42), No. 7, July 2023, pp. 1932-1943.
IEEE DOI
2307
Data models, Biomedical imaging, Task analysis, Training,
Transformers, Distributed databases, Self-supervised learning,
data efficiency
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 .