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Conventional gradient-sharing approaches for federated learning (FL), such as FedAvg, rely on aggregation of local models and often face performance degradation under differential privacy (DP) mechanisms or data heterogeneity, which can be attributed to the inconsistency between the local and global objectives.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998 · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Video data hiding for managing privacy information in surveillance systems
JithendraK Paruchuri, Sen-chingS Cheung, and MichaelW Hail. 2009 · 2009
Earlier work this paper cites.
Convolutional networks and applications in vision. In Proceedings of 2010 IEEE international symposium on circuits and systems
Yann LeCun, Koray Kavukcuoglu, and Clément Farabet. 2010 · 2010
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?. In Advances in Neural Information Processing Systems (NeurIPS)
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014 · 2014
Earlier work this paper cites.
Visual privacy protection methods: A survey
José Ramón Padilla-López, Alexandros Andre Chaaraoui, and Francisco Flórez-Revuelta. 2015 · 2015
Earlier work this paper cites.
Deep learning with differential privacy. In Proceedings of the ACM Conference on Computer and Communications Security (CCS)
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Earlier work this paper cites.
Deep models under the GAN: information leakage from collaborative deep learning. In Proceedings of the ACM Conference on Computer and Communications Security (CCS)
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz. 2017 · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
Earlier work this paper cites.
Rényi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF)
Ilya Mironov. 2017 · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017 · 2017
Earlier work this paper cites.
Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018 · 2018
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. 2018 · 2018
Earlier work this paper cites.
Group normalization. In Proceedings of the European Conference on Computer Vision (ECCV)
Yuxin Wu and Kaiming He. 2018 · 2018
Earlier work this paper cites.
Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018 · 2018
Earlier work this paper cites.
Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019 · 2019
Earlier work this paper cites.
First analysis of local gd on heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik. 2019 · 2019
Earlier work this paper cites.
Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang. 2019 · 2019
Earlier work this paper cites.
On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2019 · 2019
Earlier work this paper cites.
Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE symposium on security and privacy (SP)
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
Cited alongside, same era.
R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang. 2019 · 2019
Cited alongside, same era.
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)
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
Cited alongside, same era.
Private federated learning with domain adaptation
Daniel Peterson, Pallika Kanani, and Virendra J Marathe. 2019 · 2019
Cited alongside, same era.
No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, and Jiashi Feng. 2021 · 2021
Later among the works it cites.
Adaptive Federated Optimization. In Proceedings of the International Conference on Learning Representations (ICLR)
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan. 2021 · 2021
Later among the works it cites.
Infoscrub: Towards attribute privacy by targeted obfuscation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Hui-Po Wang, Tribhuvanesh Orekondy, and Mario Fritz. 2021 · 2021
Later among the works it cites.
Dataset condensation with differentiable siamese augmentation. In Proceedings of the International Conference on Machine Learning (ICML)
Bo Zhao and Hakan Bilen. 2021 · 2021
Later among the works it cites.
Dataset Condensation with Gradient Matching. In Proceedings of the International Conference on Learning Representations (ICLR)
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Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou. 2019 · 2019
Cited alongside, same era.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han. 2019 · 2019
Cited alongside, same era.
Hypothesis testing interpretations and renyi differential privacy. In International Conference on Artificial Intelligence and Statistics
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato. 2020 · 2020
Cited alongside, same era.
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020 · 2020
Cited alongside, same era.
Inverting gradients-how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller. 2020 · 2020
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning. In Proceedings of the International Conference on Machine Learning (ICML)
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. 2020 · 2020
Cited alongside, same era.
ML Privacy Meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning. In Workshop on Hot Topics in Privacy Enhancing Technologies (HotPETs)
Sasi Kumar and Reza Shokri. 2020 · 2020
Cited alongside, same era.
Label leakage and protection in two-party split learning
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, and Chong Wang. 2020b · 2020
Cited alongside, same era.
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2021 · 2021
Later among the works it cites.
Dataset distillation by matching training trajectories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. 2022 · 2022
Later among the works it cites.
Private Set Generation with Discriminative Information. In Advances in Neural Information Processing Systems (NeurIPS)
Dingfan Chen, Raouf Kerkouche, and Mario Fritz. 2022 · 2022
Later among the works it cites.
Privacy for free: How does dataset condensation help privacy?. In Proceedings of the International Conference on Machine Learning (ICML)
Tian Dong, Bo Zhao, and Lingjuan Lyu. 2022 · 2022
Later among the works it cites.
Label Inference Attacks Against Vertical Federated Learning. In 31st USENIX Security Symposium (USENIX Security 22)
Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex X Liu, and Ting Wang. 2022 · 2022
Later among the works it cites.
LoRA: Low-Rank Adaptation of Large Language Models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 . OpenReview.net
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Later among the works it cites.
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
Later among the works it cites.
User-Level Label Leakage from Gradients in Federated Learning
Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, and Max Mühlhäuser. 2022 · 2022
Later among the works it cites.
ProgFed: effective, communication, and computation efficient federated learning by progressive training. In Proceedings of the International Conference on Machine Learning (ICML)
Hui-Po Wang, Sebastian Stich, Yang He, and Mario Fritz. 2022 · 2022
Later among the works it cites.
Differentially Private Fine-tuning of Language Models. In Proceedings of the International Conference on Learning Representations (ICLR)
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang. 2022 · 2022
Later among the works it cites.
Generalizing Dataset Distillation via Deep Generative Prior. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. 2023 · 2023
Closest in time.
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Filippo Galli, Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi, and Tommaso Cucinotta. 2023 · 2023
Closest in time.
On the Efficacy of Differentially Private Few-shot Image Classification
Marlon Tobaben, Aliaksandra Shysheya, John Bronskill, Andrew Paverd, Shruti Tople, Santiago Zanella Béguelin, Richard E. Turner, and Antti Honkela. 2023 · 2023
Closest in time.
Feddm: Iterative distribution matching for communication-efficient federated learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, and Cho-Jui Hsieh. 2023 · 2023
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Yuchen Yang, Bo Hui, Haolin Yuan, Neil Gong, and Yinzhi Cao. 2023 · 2023
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Dataset condensation with distribution matching. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV)
Bo Zhao and Hakan Bilen. 2023 · 2023
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