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Federated Learning (FL) has emerged as a prominent distributed learning paradigm.
Membership inference attacks from first principles. In 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 1897–1914
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Learning Multiple Layers of Features from Tiny Images
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An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 215–223
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When private set intersection meets big data: an efficient and scalable protocol. In Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security . 789–800
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Building a large dataset for model-based QoE prediction in the mobile environment. In Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems . 313–317
Lamine Amour, Souihi Sami, Said Hoceini, and Abdelhamid Mellouk. 2015 · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics . PMLR, 1273–1282
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Making ai forget you: Data deletion in machine learning
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On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han. 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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How to backdoor federated learning. In International conference on artificial intelligence and statistics . PMLR, 2938–2948
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020 · 2020
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Multi-party private set intersection in vertical federated learning. In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) . IEEE, 707–714
Linpeng Lu and Ning Ding. 2020 · 2020
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Fanglan Zheng, Kun Li, Jiang Tian, Xiaojia Xiang, et al · 2020
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Machine unlearning. In 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 141–159
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. 2021 · 2021
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Amnesiac machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 11516–11524
Laura Graves, Vineel Nagisetty, and Vijay Ganesh. 2021 · 2021
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A survey on federated learning systems: Vision, hype and reality for data privacy and protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He. 2021 · 2021
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Federaser: Enabling efficient client-level data removal from federated learning models. In 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) . IEEE, 1–10
Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu. 2021b · 2021
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Fate: An industrial grade platform for collaborative learning with data protection
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The UCI machine learning repository
Markelle Kelly, Rachel Longjohn, and Kolby Nottingham. 2023 · 2023
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Subspace based federated unlearning
Guanghao Li, Li Shen, Yan Sun, Yue Hu, Han Hu, and Dacheng Tao. 2023 · 2023
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Fast yet effective machine unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli. 2023 · 2023
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Federated unlearning and its privacy threats
Fei Wang, Baochun Li, and Bo Li. 2023 · 2023
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Gradient Ascent Post-training Enhances Language Model Generalization. In The 61st Annual Meeting Of The Association For Computational Linguistics
Dongkeun Yoon, Joel Jang, Sungdong Kim, and Minjoon Seo. 2023 · 2023
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A review on machine unlearning
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A systematic review of federated learning in the healthcare area: From the perspective of data properties and applications
Prayitno, Chi-Ren Shyu, Karisma Trinanda Putra, Hsing-Chung Chen, Yuan-Yu Tsai, KSM Tozammel Hossain, Wei Jiang, and Zon-Yin Shae. 2021 · 2021
Cited alongside, same era.
Machine unlearning of features and labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck. 2021 · 2021
Cited alongside, same era.
Federated unlearning: How to efficiently erase a client in fl?
Anisa Halimi, Swanand Kadhe, Ambrish Rawat, and Nathalie Baracaldo. 2022 · 2022
Cited alongside, same era.
Membership inference attacks on machine learning: A survey
Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S Yu, and Xuyun Zhang. 2022 · 2022
Cited alongside, same era.
Randomized stochastic gradient descent ascent. In International Conference on Artificial Intelligence and Statistics . PMLR, 2941–2969
Othmane Sebbouh, Marco Cuturi, and Gabriel Peyré. 2022 · 2022
Cited alongside, same era.
Unrolling sgd: Understanding factors influencing machine unlearning. In 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P) . IEEE, 303–319
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot. 2022 · 2022
Cited alongside, same era.
Vertical federated learning: Challenges, methodologies and experiments
Kang Wei, Jun Li, Chuan Ma, Ming Ding, Sha Wei, Fan Wu, Guihai Chen, and Thilina Ranbaduge. 2022 · 2022
Cited alongside, same era.
Haibo Zhang, Toru Nakamura, Takamasa Isohara, and Kouichi Sakurai. 2023 · 2023
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Heterogeneous federated knowledge graph embedding learning and unlearning. In Proceedings of the ACM web conference 2023 . 2444–2454
Xiangrong Zhu, Guangyao Li, and Wei Hu. 2023 · 2023
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Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 19832–19839
Dashan Gao, Sheng Wan, Lixin Fan, Xin Yao, and Qiang Yang. 2024 · 2024
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Towards efficient and certified recovery from poisoning attacks in federated learning
Yu Jiang, Jiyuan Shen, Ziyao Liu, Chee Wei Tan, and Kwok-Yan Lam. 2024 · 2024
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Vertical federated learning: Concepts, advances, and challenges
Yang Liu, Yan Kang, Tianyuan Zou, Yanhong Pu, Yuanqin He, Xiaozhou Ye, Ye Ouyang, Ya-Qin Zhang, and Qiang Yang. 2024 · 2024
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Efficient Vertical Federated Unlearning via Fast Retraining
Zichen Wang, Xiangshan Gao, Cong Wang, Peng Cheng, and Jiming Chen. 2024 · 2024
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Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
Zhaomin Wu, Junyi Hou, Yiqun Diao, and Bingsheng He. 2024 · 2024
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Communication-Efficient Hybrid Federated Learning for E-Health With Horizontal and Vertical Data Partitioning
Chong Yu, Shuaiqi Shen, Shiqiang Wang, Kuan Zhang, and Hai Zhao. 2024 · 2024
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