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In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research.
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Prochlo: Strong privacy for analytics in the crowd. In Proceedings of the 26th symposium on operating systems principles . 441–459
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Practical secure aggregation for privacy-preserving machine learning. In proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . 1175–1191
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Differentially private federated learning: A client level perspective
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Learning differentially private recurrent language models
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SEMI-SUPERVISED KNOWLEDGE TRANSFER FOR DEEP LEARNING FROM PRIVATE TRAINING DATA
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Private continual release of real-valued data streams
Victor Perrier, Hassan Jameel Asghar, and Dali Kaafar. 2018 · 2018
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Privacy enhanced matrix factorization for recommendation with local differential privacy
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Federated collaborative filtering for privacy-preserving personalized recommendation system
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The privacy blanket of the shuffle model. In Advances in Cryptology–CRYPTO 2019: 39th Annual International Cryptology Conference, Santa Barbara, CA, USA, August 18–22, 2019, Proceedings, Part II 39 . Springer, 638–667
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Domain-specific batch normalization for unsupervised domain adaptation. In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition . 7354–7362
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Gmail smart compose: Real-time assisted writing. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2287–2295
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Distributed differential privacy via shuffling. In Advances in Cryptology–EUROCRYPT 2019: 38th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Darmstadt, Germany, May 19–23, 2019, Proceedings, Part I 38 . Springer, 375–403
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Differential privacy-enabled federated learning for sensitive health data
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis, Issa Sylla, Yoonyoung Park, Grace Hsu, and Amar Das. 2019 · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity. In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms . SIAM, 2468–2479
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta. 2019 · 2019
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Secure and utility-aware data collection with condensed local differential privacy
Mehmet Emre Gursoy, Acar Tamersoy, Stacey Truex, Wenqi Wei, and Ling Liu. 2019 · 2019
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On the convergence of local descent methods in federated learning
Farzin Haddadpour and Mehrdad Mahdavi. 2019 · 2019
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FDML: A collaborative machine learning framework for distributed features. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining . 2232–2240
Yaochen Hu, Di Niu, Jianming Yang, and Shengping Zhou. 2019 · 2019
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DP-ADMM: ADMM-based distributed learning with differential privacy
Zonghao Huang, Rui Hu, Yuanxiong Guo, Eric Chan-Tin, and Yanmin Gong. 2019 · 2019
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Differentially private asynchronous federated learning for mobile edge computing in urban informatics
Yunlong Lu, Xiaohong Huang, Yueyue Dai, Sabita Maharjan, and Yan Zhang. 2019 · 2019
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An overview of deep learning in medical imaging focusing on MRI
Alexander Selvikvag Lundervold and Arvid Lundervold. 2019 · 2019
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Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE symposium on security and privacy (SP) . IEEE, 691–706
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In 2019 IEEE symposium on security and privacy (SP) . IEEE, 739–753
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Network and Distributed Systems Security (NDSS) Symposium 2019
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On sparse linear regression in the local differential privacy model. In International Conference on Machine Learning . PMLR, 6628–6637
Di Wang and Jinhui Xu. 2019 · 2019
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Collecting and analyzing multidimensional data with local differential privacy. In 2019 IEEE 35th International Conference on Data Engineering (ICDE) . IEEE, 638–649
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Beyond inferring class representatives: User-level privacy leakage from federated learning. In IEEE INFOCOM 2019-IEEE conference on computer communications . IEEE, 2512–2520
Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi. 2019a · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019 · 2019
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Helen: Maliciously secure coopetitive learning for linear models. In 2019 IEEE symposium on security and privacy (SP) . IEEE, 724–738
Wenting Zheng, Raluca Ada Popa, Joseph E Gonzalez, and Ion Stoica. 2019 · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han. 2019 · 2019
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Poission subsampled rényi differential privacy. In International Conference on Machine Learning . PMLR, 7634–7642
Yuqing Zhu and Yu-Xiang Wang. 2019 · 2019
Cited alongside, same era.
Hypothesis testing interpretations and renyi differential privacy. In International Conference on Artificial Intelligence and Statistics . PMLR, 2496–2506
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato. 2020 · 2020
Cited alongside, same era.
The discrete gaussian for differential privacy
Clément L Canonne, Gautam Kamath, and Thomas Steinke. 2020 · 2020
Cited alongside, same era.
Vafl: a method of vertical asynchronous federated learning
Tianyi Chen, Xiao Jin, Yuejiao Sun, and Wotao Yin. 2020 · 2020
Cited alongside, same era.
Signds-fl: Local differentially private federated learning with sign-based dimension selection
Xue Jiang, Xuebing Zhou, and Jens Grossklags. 2022 · 2022
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OpBoost: a vertical federated tree boosting framework based on order-preserving desensitization
Xiaochen Li, Yuke Hu, Weiran Liu, Hanwen Feng, Li Peng, Yuan Hong, Kui Ren, and Zhan Qin. 2022a · 2022
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SoteriaFL: A unified framework for private federated learning with communication compression
Zhize Li, Haoyu Zhao, Boyue Li, and Yuejie Chi. 2022c · 2022
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Shuffled check-in: privacy amplification towards practical distributed learning
Seng Pei Liew, Satoshi Hasegawa, and Tsubasa Takahashi. 2022 · 2022
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Towards private learning on decentralized graphs with local differential privacy
Wanyu Lin, Baochun Li, and Cong Wang. 2022 · 2022
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Smoothly bounding user contributions in differential privacy
Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, and Lijie Ren. 2020 · 2020
Cited alongside, same era.
Does learning require memorization? a short tale about a long tail. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing . 954–959
Vitaly Feldman. 2020 · 2020
Cited alongside, same era.
Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. 2020 · 2020
Cited alongside, same era.
DP-FL: a novel differentially private federated learning framework for the unbalanced data
Xixi Huang, Ye Ding, Zoe L Jiang, Shuhan Qi, Xuan Wang, and Qing Liao. 2020 · 2020
Cited alongside, same era.
Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea. 2020 · 2020
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning. In International conference on machine learning . PMLR, 5132–5143
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. 2020 · 2020
Cited alongside, same era.
Fedsel: Federated sgd under local differential privacy with top-k dimension selection. In Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings, Part I 25 . Springer, 485–501
Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa, and Hong Chen. 2020a · 2020
Cited alongside, same era.
Later among the works it cites.
Local differential privacy for federated learning. In European Symposium on Research in Computer Security . Springer, 195–216
Pathum Chamikara Mahawaga Arachchige, Dongxi Liu, Seyit Camtepe, Surya Nepal, Marthie Grobler, Peter Bertok, and Ibrahim Khalil. 2022 · 2022
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Secure Split Learning against Property Inference and Data Reconstruction Attacks
Yunlong Mao, Zexi Xin, Zhenyu Li, Jue Hong, Yang Qingyou, and Sheng Zhong. 2022 · 2022
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Differentially private federated learning on heterogeneous data. In International Conference on Artificial Intelligence and Statistics . PMLR, 10110–10145
Maxence Noble, Aurélien Bellet, and Aymeric Dieuleveut. 2022 · 2022
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Differentially private cutmix for split learning with vision transformer
Seungeun Oh, Jihong Park, Sihun Baek, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, and Seong-Lyun Kim. 2022 · 2022
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Differentially private federated learning via inexact ADMM with multiple local updates
Minseok Ryu and Kibaek Kim. 2022 · 2022
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Efficient differentially private secure aggregation for federated learning via hardness of learning with errors. In 31st USENIX Security Symposium (USENIX Security 22) . 1379–1395
Timothy Stevens, Christian Skalka, Christelle Vincent, John Ring, Samuel Clark, and Joseph Near. 2022 · 2022
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Defending against Reconstruction Attacks with Rényi Differential Privacy
Pierre Stock, Igor Shilov, Ilya Mironov, and Alexandre Sablayrolles. 2022 · 2022
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Seqpate: Differentially private text generation via knowledge distillation
Zhiliang Tian, Yingxiu Zhao, Ziyue Huang, Yu-Xiang Wang, Nevin L Zhang, and He He. 2022 · 2022
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Safeguarding cross-silo federated learning with local differential privacy
Chen Wang, Xinkui Wu, Gaoyang Liu, Tianping Deng, Kai Peng, and Shaohua Wan. 2022b · 2022
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L-srr: Local differential privacy for location-based services with staircase randomized response. In Proceedings of the 2022 ACM SIGSAC Conference on computer and communications security . 2809–2823
Han Wang, Hanbin Hong, Li Xiong, Zhan Qin, and Yuan Hong. 2022a · 2022
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Dpis: An enhanced mechanism for differentially private sgd with importance sampling. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security . 2885–2899
Jianxin Wei, Ergute Bao, Xiaokui Xiao, and Yin Yang. 2022 · 2022
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Edge resource prediction and auction for distributed spatial crowdsourcing with differential privacy
Yin Xu, Mingjun Xiao, An Liu, and Jie Wu. 2022 · 2022
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Differentially private label protection in split learning
Xin Yang, Jiankai Sun, Yuanshun Yao, Junyuan Xie, and Chong Wang. 2022 · 2022
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Understanding clipping for federated learning: Convergence and client-level differential privacy. In International Conference on Machine Learning, ICML 2022
Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, and Jinfeng Yi. 2022 · 2022
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Privacy-Enhanced Federated Learning: A Restrictively Self-Sampled and Data-Perturbed Local Differential Privacy Method
Jianzhe Zhao, Mengbo Yang, Ronglin Zhang, Wuganjing Song, Jiali Zheng, Jingran Feng, and Stan Matwin. 2022 · 2022
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Multimodal federated learning: A survey
Liwei Che, Jiaqi Wang, Yao Zhou, and Fenglong Ma. 2023 · 2023
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Differential privacy in personalized pricing with nonparametric demand models
Xi Chen, Sentao Miao, and Yining Wang. 2023b · 2023
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Jie Fu, Qingqing Ye, Haibo Hu, Zhili Chen, Lulu Wang, Kuncan Wang, and Ran Xun. 2023 · 2023
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Clustered federated learning with adaptive local differential privacy on heterogeneous iot data
Zaobo He, Lintao Wang, and Zhipeng Cai. 2023 · 2023
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Stochastic distributed learning with gradient quantization and double-variance reduction
Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik, and Sebastian Stich. 2023 · 2023
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Olive: Oblivious Federated Learning on Trusted Execution Environment against the Risk of Sparsification
Fumiyuki Kato, Yang Cao, and Masatoshi Yoshikawa. 2023 · 2023
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Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model. In Proceedings of the AAAI Conference on Artificial Intelligence
Yixuan Liu, Suyun Zhao, Li Xiong, Yuhan Liu, and Hong Chen. 2023 · 2023
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Tight auditing of differentially private machine learning. In 32nd USENIX Security Symposium (USENIX Security 23) . 1631–1648
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, and Andreas Terzis. 2023 · 2023
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Differentially private knowledge transfer for federated learning
Tao Qi, Fangzhao Wu, Chuhan Wu, Liang He, Yongfeng Huang, and Xing Xie. 2023 · 2023
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{ \{ GAP } \} : Differentially Private Graph Neural Networks with Aggregation Perturbation. In 32nd USENIX Security Symposium (USENIX Security 23) . 3223–3240
Sina Sajadmanesh, Ali Shahin Shamsabadi, Aurélien Bellet, and Daniel Gatica-Perez. 2023 · 2023
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SoK: Let the privacy games begin! A unified treatment of data inference privacy in machine learning. In 2023 IEEE Symposium on Security and Privacy (SP) . IEEE, 327–345
Ahmed Salem, Giovanni Cherubin, David Evans, Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, and Santiago Zanella-Béguelin. 2023 · 2023
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Make landscape flatter in differentially private federated learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 24552–24562
Yifan Shi, Yingqi Liu, Kang Wei, Li Shen, Xueqian Wang, and Dacheng Tao. 2023 · 2023
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Eliminating Label Leakage in Tree-Based Vertical Federated Learning
Hideaki Takahashi, Jingjing Liu, and Yang Liu. 2023 · 2023
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FederBoost: Private Federated Learning for GBDT
Zhihua Tian, Rui Zhang, Xiaoyang Hou, Lingjuan Lyu, Tianyi Zhang, Jian Liu, and Kui Ren. 2023 · 2023
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FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation
Sheng Wan, Dashan Gao, Hanlin Gu, and Daning Hu. 2023 · 2023
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PPeFL: Privacy-Preserving Edge Federated Learning with Local Differential Privacy
Baocang Wang, Yange Chen, Hang Jiang, and Zhen Zhao. 2023a · 2023
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Can Public Large Language Models Help Private Cross-device Federated Learning?
Boxin Wang, Yibo Jacky Zhang, Yuan Cao, Bo Li, H Brendan McMahan, Sewoong Oh, Zheng Xu, and Manzil Zaheer. 2023c · 2023
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Generalized linear models in non-interactive local differential privacy with public data
Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, and Jinhui Xu. 2023b · 2023
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Practical Differentially Private and Byzantine-resilient Federated Learning
Zihang Xiang, Tianhao Wang, Wanyu Lin, and Di Wang. 2023 · 2023
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Federated learning of gboard language models with differential privacy
Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher A Choquette-Choo, Peter Kairouz, H Brendan McMahan, Jesse Rosenstock, and Yuanbo Zhang. 2023b · 2023
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Personalized Federated Learning Method Based on Bregman Divergence and Differential Privacy (in chinese)
Shaobo Zhang, Jiyong Zhang, Gengming Zhu, Saiqin Long, and Li Zhetao. 2023b · 2023
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A systematic survey for differential privacy techniques in federated learning
Yi Zhang, Yunfan Lu, and Fengxia Liu. 2023a · 2023
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Differentially private federated learning on non-iid data: Convergence analysis and adaptive optimization
Lin Chen, Xiaofeng Ding, Zhifeng Bao, Pan Zhou, and Hai Jin. 2024 · 2024
Closest in time.
Differential privacy based federated learning techniques in IoMT: A review. In 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) . IEEE, 1–7
Shaista Ashraf Farooqi, Aedah Abd Rahman, and Amna Saad. 2024 · 2024
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Students parrot their teachers: Membership inference on model distillation
Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A Choquette-Choo, Nicholas Carlini, and Florian Tramer. 2024 · 2024
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ULDP-FL: Federated Learning with Across-Silo User-Level Differential Privacy. In Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases , Vol. 17. 2826
Fumiyuki Kato, Li Xiong, Shun Takagi, Yang Cao, and Masatoshi Yoshikawa. 2024 · 2024
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Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
Yipeng Li and Xinchen Lyu. 2024 · 2024
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ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations. In 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) . IEEE, 349–358
Xinpeng Ling, Jie Fu, Kuncan Wang, Haitao Liu, and Zhili Chen. 2024 · 2024
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Differentially private parameter-efficient fine-tuning for large asr models
Hongbin Liu, Lun Wang, Om Thakkar, Abhradeep Thakurta, and Arun Narayanan. 2024c · 2024
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Cross-silo federated learning with record-level personalized differential privacy. In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security . 303–317
Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, and Xiaofeng Meng. 2024b · 2024
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Federated graph analytics with differential privacy
Shang Liu, Yang Cao, Takao Murakami, Weiran Liu, Seng Pei Liew, Tsubasa Takahashi, Jinfei Liu, and Masatoshi Yoshikawa. 2024a · 2024
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Noise-aware algorithm for heterogeneous differentially private federated learning. In Proceedings of the 41st International Conference on Machine Learning . 34461–34498
Saber Malekmohammadi, Yaoliang Yu, and Yang Cao. 2024 · 2024
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Belt and braces: When federated learning meets differential privacy
Xuebin Ren, Shusen Yang, Cong Zhao, Julie McCann, and Zongben Xu. 2024 · 2024
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Private over-the-air federated learning at band-limited edge
Youming Tao, Shuzhen Chen, Congwei Zhang, Di Wang, Dongxiao Yu, Xiuzhen Cheng, and Falko Dressler. 2024 · 2024
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Towards Accurate and Stronger Local Differential Privacy for Federated Learning with Staircase Randomized Response. In 14th ACM Conference on Data and Application Security and Privacy . ACM
Matta Varun, Shuya Feng, Han Wang, Shamik Sural, and Yuan Hong. 2024 · 2024
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Linkteller: Recovering private edges from graph neural networks via influence analysis. In 2022 ieee symposium on security and privacy (sp) . IEEE, 2005–2024
Fan Wu, Yunhui Long, Ce Zhang, and Bo Li. 2022 · 2024
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Dynamic privacy allocation for locally differentially private federated learning with composite objectives. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 9461–9465
Jiaojiao Zhang, Dominik Fay, and Mikael Johansson. 2024 · 2024
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PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data
Juexiao Zhou, Siyuan Chen, Yulian Wu, Haoyang Li, Bin Zhang, Longxi Zhou, Yan Hu, Zihang Xiang, Zhongxiao Li, Ningning Chen, et al · 2024
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Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 39. 15293–15301
Amr Abourayya, Jens Kleesiek, Kanishka Rao, Erman Ayday, Bharat Rao, Geoffrey I Webb, and Michael Kamp. 2025 · 2025
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Unlocking the Power of Differentially Private Zeroth-order Optimization for Fine-tuning { \{ LLMs } \} . In 34th USENIX Security Symposium (USENIX Security 25) . 1569–1588
Ergute Bao, Yangfan Jiang, Fei Wei, Xiaokui Xiao, Zitao Li, Yaliang Li, and Bolin Ding. 2025 · 2025
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Privacy-preserving vertical federated learning with tensor decomposition for data missing features
Tianchi Liao, Lele Fu, Lei Zhang, Lei Yang, Chuan Chen, Michael K Ng, Huawei Huang, and Zibin Zheng. 2025 · 2025
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Differentially private low-rank adaptation of large language model using federated learning
Xiao-Yang Liu, Rongyi Zhu, Daochen Zha, Jiechao Gao, Shan Zhong, Matt White, and Meikang Qiu. 2025 · 2025
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Selective Privacy-Preserving Federated Learning for Large Language Model Fine-Tuning. In 2025 International Wireless Communications and Mobile Computing (IWCMC) . IEEE, 1626–1631
Qianqian Pan and Jun Wu. 2025 · 2025
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Webfed: Cross-platform federated learning framework based on web browser with local differential privacy. In ICC 2022-IEEE International Conference on Communications . IEEE, 2071–2076
Zhuotao Lian, Qinglin Yang, Qingkui Zeng, and Chunhua Su. 2022 · 2076
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