*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

Springer DOI

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 .

Last update:Aug 31, 2023 at 09:37:21