14.1.14.2.2 Privacy in Federated Learning

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

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

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

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

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.Y.[Yu-Yin],
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

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.L.[Daniel L.],
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

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.
WWW Link. 2401
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

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

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

Piotrowski, T.[Tomasz], Ismayilov, R.[Rafail], Frey, M.[Matthias], Cavalcante, R.L.G.[Renato L.G.],
Inverse Feasibility in Over-the-Air Federated Learning,
SPLetters(31), 2024, pp. 1434-1438.
IEEE DOI 2405
Servers, Security, Computational modeling, Privacy, Inverse problems, Communication system security, Wireless networks, inverse problems BibRef

Ye, Q.L.[Qiao-Ling], Amini, A.A.[Arash A.], Zhou, Q.[Qing],
Federated Learning of Generalized Linear Causal Networks,
PAMI(46), No. 10, October 2024, pp. 6623-6636.
IEEE DOI 2409
Distributed databases, Optimization, Federated learning, Data privacy, Data models, Servers, Simulated annealing, topological sorts BibRef

Liu, X.[Xuan], Cai, S.Q.[Si-Qi], He, R.J.[Ren-Jie], Yuan, J.L.[Jing-Ling],
Mutual Gradient Inversion: Unveiling Privacy Risks of Federated Learning on Multi-Modal Signals,
SPLetters(31), 2024, pp. 2745-2749.
IEEE DOI 2410
Training, Data models, Federated learning, Servers, Threat modeling, Image reconstruction, Differential privacy, Federated learning, privacy leakage BibRef

Zhou, T.F.[Tian-Fei], Yuan, Y.[Ye], Wang, B.L.[Bing-Lu], Konukoglu, E.[Ender],
Federated Feature Augmentation and Alignment,
PAMI(46), No. 12, December 2024, pp. 11119-11135.
IEEE DOI 2411
Training, Data models, Transformers, Prototypes, Federated learning, Degradation, Data privacy, Feature alignment, feature augmentation, federated learning BibRef

You, X.Y.[Xian-Yao], Liu, C.Y.[Cai-Yun], Li, J.[Jun], Sun, Y.[Yan], Liu, X.M.[Xi-Meng],
FedMDO: Privacy-Preserving Federated Learning via Mixup Differential Objective,
CirSysVideo(34), No. 10, October 2024, pp. 10449-10463.
IEEE DOI 2411
Federated learning, Differential privacy, Protection, Training, Servers, Privacy, Task analysis, Federated learning, data privacy, data augmentation BibRef

Fang, X.W.[Xiu-Wen], Ye, M.[Mang], Du, B.[Bo],
Robust Asymmetric Heterogeneous Federated Learning With Corrupted Clients,
PAMI(47), No. 4, April 2025, pp. 2693-2705.
IEEE DOI 2503
BibRef
Earlier: A1, A2, Only:
Robust Federated Learning with Noisy and Heterogeneous Clients,
CVPR22(10062-10071)
IEEE DOI 2210
Data models, Computational modeling, Noise, Federated learning, Robustness, Training, Data augmentation, Analytical models. Adaptation models, Privacy, Collaboration, Collaborative work, Federated learning BibRef

Jin, S.[Suyeon], Cha, C.[Chaeyeon], Park, H.[Hyunggon],
Alternating Offer-Based Payment Allocation for Privacy Non-Disclosure in Federated Learning,
SPLetters(32), 2025, pp. 1500-1504.
IEEE DOI 2504
Servers, Resource management, Games, Costs, Training, Accuracy, Privacy, NIST, Computational modeling, Vectors, Federated learning, alternating-offers BibRef

Dai, C.[Cheng], Zhu, T.[Tianli], Xiang, S.[Sha], Xie, L.P.[Li-Ping], Garg, S.[Sahil], Hossain, M.S.[M. Shamim],
PSFL: Personalized Split Federated Learning Framework for Distributed Model Training in Intelligent Transportation Systems,
ITS(26), No. 9, September 2025, pp. 14110-14119.
IEEE DOI 2510
Computational modeling, Servers, Training, Data models, Data privacy, Adaptation models, Federated learning, 6G mobile communication, non-independent and identically distributed (non-IID) data BibRef

Zhang, C.J.[Chun-Jiong], Shan, G.Y.[Gao-Yang], Roh, B.H.[Byeong-Hee],
FMD-IoV: Security and Robust Enhancement for Federated Multi-Domain Learning-Based IoV,
ITS(26), No. 9, September 2025, pp. 14225-14236.
IEEE DOI 2510
Data models, Feature extraction, Training, Soft sensors, Convergence, Automobiles, Object detection, Data privacy, generalizability BibRef

Ha, S.B.[Seung-Bum], Lee, T.[Taehwan], Lim, J.[Jiyoun], Yoon, S.W.[Sung Whan],
Benchmarking federated learning for semantic datasets: Federated scene graph generation,
PRL(197), 2025, pp. 195-201.
Elsevier DOI Code:
WWW Link. 2510
Scene graph generation, Panoptic scene graph generation, Federated learning, Distributed learning, Data privacy, Benchmark BibRef

Qin, X.[Xian], Yang, X.[Xue], Tang, X.[Xiaohu],
Practical privacy-preserving federated learning based on multiparty homomorphic encryption for large-scale models,
PR(171), 2026, pp. 112174.
Elsevier DOI 2511
Federated learning, Privacy-preserving, Secure aggregation, Robustness against client collusion, Dropout-resiliency guarantee BibRef

Shi, Y.F.[Yi-Fan], Wei, K.[Kang], Shen, L.[Li], Liu, Y.Q.[Ying-Qi], Wang, X.Q.[Xue-Qian], Yuan, B.[Bo], Tao, D.C.[Da-Cheng],
Toward the Flatter Landscape and Better Generalization in Federated Learning Under Client-Level Differential Privacy,
PAMI(47), No. 12, December 2025, pp. 11632-11643.
IEEE DOI 2511
Noise, Privacy, Perturbation methods, Degradation, Robustness, Training, Convergence, Protection, Data models, Federated learning, local update sparsification BibRef

Yoon, T.[Tehrim], Hwang, M.Y.[Min-Young], Yang, E.[Eunho],
VQ-FedDiff: Federated Learning Algorithm of Diffusion Models With Client-Specific Vector-Quantized Conditioning,
PAMI(47), No. 12, December 2025, pp. 11863-11873.
IEEE DOI 2511
Diffusion models, Data models, Training, Servers, Federated learning, Privacy, Data privacy, Computational modeling, Measurement BibRef

Liu, J.Y.[Ji-Yuan], Liu, X.W.[Xin-Wang], Wang, S.Q.[Si-Qi], Wan, X.H.[Xin-Hang], Li, D.S.[Dong-Sheng], Lu, K.[Kai], He, K.L.[Kun-Lun],
Communication-Efficient Federated Multi-View Clustering,
PAMI(48), No. 1, January 2026, pp. 17-32.
IEEE DOI 2512
Servers, Data visualization, Kernel, Computational complexity, Encoding, Training, Distributed databases, Data privacy, matrix approximation BibRef

Pennisi, M.[Matteo], Salanitri, F.P.[Federica Proietto], Bellitto, G.[Giovanni], Casella, B.[Bruno], Aldinucci, M.[Marco], Palazzo, S.[Simone], 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 and classification BibRef

Qian, W.J.[Wen-Jun], Yu, W.[Wei], Ge, X.[Xin], Li, C.[Cong], Li, L.[Lianyuan], Li, Z.[Zheng],
PDP-FedKD: Personalized Differential Privacy With Adaptive Budget Selection in Heterogeneous Federated Learning,
SPLetters(33), 2026, pp. 166-170.
IEEE DOI 2601
Privacy, Differential privacy, Training, Servers, Data models, Protection, Adaptation models, Gaussian noise, Federated learning, budget self-selection BibRef

Pereira, L.M.[Luiz Manella], Amini, M.H.[M. Hadi],
Optimal Transport-Based Domain Alignment as a Preprocessing Step for Federated Learning,
ICIP25(1049-1054)
IEEE DOI 2601
Performance evaluation, Data privacy, Accuracy, Federated learning, Scalability, Computational modeling, Prediction algorithms, Image Preprocessing BibRef

Fraux, D.[David], Druart, A.[Anaïs], Marat, S.G.[Sophie Guegan],
FeDepthX: A Federated Learning Depth eXperiment,
IPTA25(1-6)
IEEE DOI 2601
Training, Data privacy, Accuracy, Federated learning, Depth measurement, Computational modeling, Training data, privacy preservation BibRef

Kumar, K.N.[K. Naveen], Jha, R.R.[Ranjeet Ranjan], Mohan, C.K.[C. Krishna], Tallamraju, R.B.[Ravindra Babu],
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution,
CVPR25(4999-5009)
IEEE DOI 2508
Training, Data privacy, Federated learning, Gaussian noise, Density functional theory, Servers, Security, Cost accounting, data poisoning threat BibRef

Kumar, K.N.[K. Naveen], Mitra, R.[Reshmi], Mohan, C.K.[C. Krishna],
Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space,
CVPR24(24387-24397)
IEEE DOI 2410
Training, Data privacy, Privacy, Federated learning, Computational modeling, Predictive models, Data models, Defenders perspective BibRef

Zhu, G.X.[Gong-Xi], Li, D.H.[Dong-Hao], Gu, H.L.[Han-Lin], Yao, Y.[Yuan], Fan, L.X.[Li-Xin], Han, Y.X.[Yu-Xing],
FedMIA: An Effective Membership Inference Attack Exploiting 'All for One' Principle in Federated Learning,
CVPR25(20643-20653)
IEEE DOI Code:
WWW Link. 2508
Training, Measurement, Data privacy, Codes, Federated learning, Buildings, Focusing, Data models, membership inference attack, federated learning BibRef

Tan, Z.[Zihan], Wan, G.C.[Guan-Cheng], Huang, W.K.[Wen-Ke], Li, H.[He], Zhang, G.[Guibin], Yang, C.[Carl], Ye, M.[Mang],
FedSPA : Generalizable Federated Graph Learning under Homophily Heterogeneity,
CVPR25(15464-15475)
IEEE DOI Code:
WWW Link. 2508
Training, Data privacy, Codes, Collaboration, Graph neural networks, federated learning BibRef

Xie, L.[Luyuan], Luan, T.Y.[Tian-Yu], Cai, W.Y.[Wen-Yuan], Yan, G.[Guochen], Chen, Z.Y.[Zhao-Yu], Xi, N.[Nan], Fang, Y.J.[Yue-Jian], Shen, Q.[Qingni], Wu, Z.H.[Zhong-Hai], Yuan, J.S.[Jun-Song],
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis,
CVPR25(10203-10213)
IEEE DOI 2508
Training, Privacy, Head, Federated learning, Computational modeling, Back, Stability analysis, Robustness, Servers, medical data analysis BibRef

Ma, Y.B.[Yan-Biao], Dai, W.[Wei], Huang, W.K.[Wen-Ke], Chen, J.Y.[Jia-Yi],
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning,
CVPR25(20958-20968)
IEEE DOI Code:
WWW Link. 2508
Training, Privacy, Shape, Federated learning, Prototypes, Data collection, Data models, Optimization BibRef

Shi, C.L.[Chang-Long], Zhao, H.[He], Zhang, B.J.[Bing-Jie], Zhou, M.Y.[Ming-Yuan], Guo, D.D.[Dan-Dan], Chang, Y.[Yi],
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors,
CVPR25(30651-30660)
IEEE DOI 2508
Training, Adaptation models, Data privacy, Federated learning, Computational modeling, Vectors, Data models, Stability analysis, machine learning BibRef

Chen, H.[Haokun], Li, H.[Hang], Zhang, Y.[Yao], Bi, J.[Jinhe], Zhang, G.[Gengyuan], Zhang, Y.[Yueqi], Torr, P.[Philip], Gu, J.D.[Jin-Dong], Krompass, D.[Denis], Tresp, V.[Volker],
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models,
CVPR25(30440-30450)
IEEE DOI Code:
WWW Link. 2508
Data privacy, Federated learning, Scalability, Diffusion models, Data models, Regulation, Satellite images, Biomedical imaging, data augmentation BibRef

Khalil, Y.H.[Yasser H.], Brunswic, L.[Leo], Lamghari, S.[Soufiane], Li, X.[Xu], Beitollahi, M.[Mahdi], Chen, X.[Xi],
NoT: Federated Unlearning via Weight Negation,
CVPR25(25759-25769)
IEEE DOI 2508
Data privacy, Federated learning, Computational modeling, Perturbation methods, Data models, Servers, Optimization, weight negation BibRef

Yan, Y.L.[Yun-Lu], Fu, H.Z.[Hua-Zhu], Li, Y.X.[Yue-Xiang], Xie, J.H.[Jin-Heng], Ma, J.[Jun], Yang, G.[Guang], Zhu, L.[Lei],
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning,
CVPR25(25749-25758)
IEEE DOI Code:
WWW Link. 2508
Privacy, Codes, Federated learning, Distributed databases, Data augmentation, Security, federated learning, data augmentation BibRef

Caldarola, D.[Debora], Cagnasso, P.[Pietro], Caputo, B.[Barbara], Ciccone, M.[Marco],
Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning,
CVPR25(25187-25197)
IEEE DOI 2508
Training, Privacy, Federated learning, Focusing, Minimization, Robustness, Explosions, Servers, Optimization, federated learning, sharpness-aware minimization BibRef

Mendieta, M.[Matías], Sun, G.Y.[Guang-Yu], Chen, C.[Chen],
Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models,
WACV25(2601-2610)
IEEE DOI Code:
WWW Link. 2505
Training, Federated learning, Filtering, Navigation, Diffusion models, Data models, Security, Faces, Pragmatics BibRef

Kanhere, A.[Adway], Kulkarni, P.[Pranav], Yi, P.H.[Paul H.], Parekh, V.S.[Vishwa S.],
Privacy-Preserving Collaboration for Multi-Organ Segmentation via Federated Learning from Sites with Partial Labels,
EnhanceMedIm24(2380-2387)
IEEE DOI Code:
WWW Link. 2410
Spleen, Image segmentation, Annotations, Federated learning, Collaboration, Liver, Data models, federated learning, computed tomography BibRef

Tabassum, N.[Nawrin], Chow, K.H.[Ka-Ho], Wang, X.[Xuyu], Zhang, W.B.[Wen-Bin], Wu, Y.Z.[Yan-Zhao],
On the Efficiency of Privacy Attacks in Federated Learning,
FedVision244(4226-4235)
IEEE DOI Code:
WWW Link. 2410
Privacy, Data privacy, Costs, Federated learning, Training data, Benchmark testing BibRef

Soni, S.[Sunny], Saeed, A.[Aaqib], Asano, Y.M.[Yuki M.],
Federated Learning with a Single Shared Image,
ZeroShot24(7782-7790)
IEEE DOI 2410
Training, Schedules, Data privacy, Machine learning algorithms, Federated learning, Training data, Performance gain BibRef

Lee, G.[Gihun], Jeong, M.[Minchan], Kim, S.[Sangmook], Oh, J.[Jaehoon], Yun, S.Y.[Se-Young],
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning,
CVPR24(12512-12522)
IEEE DOI 2410
Degradation, Data privacy, Federated learning, Perturbation methods, Aggregates, Data models, Federated Learning, Continual Learning BibRef

Xie, C.[Chulin], Huang, D.A.[De-An], Chu, W.[Wenda], Xu, D.[Daguang], Xiao, C.W.[Chao-Wei], Li, B.[Bo], Anandkumar, A.[Anima],
Perada: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees,
CVPR24(23838-23848)
IEEE DOI Code:
WWW Link. 2410
Adaptation models, Costs, Codes, Federated learning, Computational modeling, Aggregates, Privacy BibRef

Tran, M.T.[Minh-Tuan], Le, T.[Trung], Le, X.M.[Xuan-May], Harandi, M.[Mehrtash], Phung, D.[Dinh],
Text-Enhanced Data-Free Approach for Federated Class-Incremental Learning,
CVPR24(23870-23880)
IEEE DOI Code:
WWW Link. 2410
Training, Data privacy, Codes, Federated learning, Computational modeling, Data models, federated learning, text embedding BibRef

Li, W.Q.[Wen-Qian], Fu, S.[Shuran], Zhang, F.[Fengrui], Pang, Y.[Yan],
Data Valuation and Detections in Federated Learning,
CVPR24(12027-12036)
IEEE DOI Code:
WWW Link. 2410
Training, Measurement, Data privacy, Federated learning, Computational modeling, Data models, Data Valuation, Trustworthy ML BibRef

Zhang, J.Q.[Jian-Qing], Liu, Y.[Yang], Hua, Y.[Yang], Cao, J.[Jian],
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning,
CVPR24(12109-12119)
IEEE DOI Code:
WWW Link. 2410
Training, Privacy, Federated learning, Image edge detection, Generators, Data models, large pre-trained generator, model heterogeneity BibRef

Zhao, J.C.[Joshua C.], Dabholkar, A.[Ahaan], Sharma, A.[Atul], Bagchi, S.[Saurabh],
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning,
CVPR24(12247-12256)
IEEE DOI 2410
Training, Data privacy, Federated learning, Training data, Semisupervised learning, Data models, Servers, Federated learning, privacy BibRef

Tu, N.A.[Nguyen Anh], Abu, A.[Assanali], Aikyn, N.[Nartay], Makhanov, N.[Nursultan], Lee, M.H.[Min-Ho], Le-Huy, K.[Khiem], Wong, K.S.[Kok-Seng],
FedFSLAR: A Federated Learning Framework for Few-shot Action Recognition,
RWSurvil24(270-279)
IEEE DOI 2404
Metalearning, Adaptation models, Data privacy, Correlation, Federated learning, Computational modeling BibRef

Eloul, S.[Shaltiel], Silavong, F.[Fran], Kamthe, S.[Sanket], Georgiadis, A.[Antonios], Moran, S.J.[Sean J.],
Mixing Gradients in Neural Networks as a Strategy to Enhance Privacy in Federated Learning,
WACV24(3944-3953)
IEEE DOI 2404
Measurement, Training, Resistance, Privacy, Federated learning, Noise, Neural networks, Algorithms, Adversarial learning, Datasets and evaluations BibRef

Wang, F.[Feng], Velipasalar, S.[Senem], Gursoy, M.C.[M. Cenk],
Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning,
WACV24(3872-3881)
IEEE DOI Code:
WWW Link. 2404
Data privacy, Privacy, Federated learning, Computational modeling, Neural networks, Pressing, Reconstruction algorithms, Algorithms, ethical computer vision BibRef

Sivasubramanian, D.[Durga], Nagalapatti, L.[Lokesh], Iyer, R.[Rishabh], Ramakrishnan, G.[Ganesh],
Gradient Coreset for Federated Learning,
WACV24(2636-2645)
IEEE DOI 2404
Training, Privacy, Federated learning, Computational modeling, Noise, Fitting, Training data, Algorithms, Machine learning architectures, ethical computer vision BibRef

Amosy, O.[Ohad], Eyal, G.[Gal], Chechik, G.[Gal],
Late to the party? On-demand unlabeled personalized federated learning,
WACV24(2173-2182)
IEEE DOI 2404
Training, Differential privacy, Privacy, Federated learning, Perturbation methods, Computational modeling, Algorithms BibRef

Ashraf, T.[Tajamul], Mir, F.B.A.[Fuzayil Bin Afzal], Gillani, I.A.[Iqra Altaf],
TransFed: A way to epitomize Focal Modulation using Transformer-based Federated Learning,
WACV24(543-552)
IEEE DOI 2404
Data privacy, Pneumonia, Federated learning, Scalability, Modulation, Collaboration, Algorithms, Image recognition and understanding, Biomedical / healthcare / medicine 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

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, Privacy and federated learning, Others BibRef

Tang, M.X.[Min-Xue], Ning, X.F.[Xue-Fei], Wang, Y.[Yitu], Sun, J.W.[Jing-Wei], Wang, Y.[Yu], Li, H.[Hai], Chen, Y.R.[Yi-Ran],
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning,
CVPR22(10092-10101)
IEEE DOI 2210
Training, Correlation, Federated learning, Gaussian processes, 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, 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.H.[Zhuo-Hang], 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

Xu, J.Y.[Jing-Yi], Chen, Z.H.[Zi-Han], 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

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

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

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

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

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