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
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 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
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 .