Privacy-Aware Compression for Federated Data Analysis

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Privacy-Aware Compression for Federated Data Analysis

Conference on Uncertainty in Artificial Intelligence (UAI), 2022

15 March 2022

Kamalika Chaudhuri

Michael G. Rabbat

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Papers citing "Privacy-Aware Compression for Federated Data Analysis"

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Title
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study Eric Aubinais Philippe Formont Pablo Piantanida Elisabeth Gassiat 279 1 0 10 Feb 2025
A survey on secure decentralized optimization and learning Changxin Liu Nicola Bastianello Wei Huo Yang Shi Karl H. Johansson 201 9 0 16 Aug 2024
Privacy-Aware Randomized Quantization via Linear Programming Zhongteng Cai Xueru Zhang Mohammad Mahdi Khalili 324 2 0 01 Jun 2024
Universal Exact Compression of Differentially Private Mechanisms Yanxiao Liu Wei-Ning Chen Ayfer Özgür Cheuk Ting Li 189 9 0 28 May 2024
Enhancing Privacy in Federated Learning through Local Training Nicola Bastianello Changxin Liu Karl H. Johansson 187 3 0 26 Mar 2024
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data Richeng Jin Yujie Gu Kai Yue Xiaofan He Zhaoyang Zhang Huaiyu Dai FedML 246 1 0 16 Feb 2024
Near-Linear Scaling Data Parallel Training with Overlapping-Aware Gradient Compression Lin Meng Yuzhong Sun Weimin Li 182 4 0 08 Nov 2023
Compression with Exact Error Distribution for Federated LearningInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Mahmoud Hegazy Rémi Leluc Cheuk Ting Li Hadrien Hendrikx FedML 158 17 0 31 Oct 2023
Communication Efficient Private Federated Learning Using DitheringIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023 Burak Hasircioglu Deniz Gunduz FedML 233 12 0 14 Sep 2023
Communication-Efficient Laplace Mechanism for Differential Privacy via Random QuantizationIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023 Ali Moradi Shahmiri Chih Wei Ling Jiande Sun 133 18 0 13 Sep 2023
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive NetworksIEEE Transactions on Mobile Computing (IEEE TMC), 2023 Natalie Lang Stefano Rini Rafael G. L. DÓliveira S. E. Rouayheb FedML 337 8 0 01 Aug 2023
Randomized Quantization is All You Need for Differential Privacy in Federated Learning Yeojoon Youn Zihao Hu Juba Ziani Jacob D. Abernethy FedML 144 28 0 20 Jun 2023
Fast Optimal Locally Private Mean Estimation via Random ProjectionsNeural Information Processing Systems (NeurIPS), 2023 Hilal Asi Vitaly Feldman Jelani Nelson Huy Le Nguyen Kunal Talwar FedML 207 15 0 07 Jun 2023
Multi-Message Shuffled Privacy in Federated LearningIEEE Journal on Selected Areas in Information Theory (JSAIT), 2023 Antonious M. Girgis Suhas Diggavi FedML 209 10 0 22 Feb 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$ -Differential PrivacyNeural Information Processing Systems (NeurIPS), 2023 Richeng Jin Z. Su C. Zhong Zhaoyang Zhang Tony Q.S. Quek H. Dai FedML 241 3 0 19 Feb 2023
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism DesignInternational Conference on Machine Learning (ICML), 2022 Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 245 7 0 08 Nov 2022
On the Interaction Between Differential Privacy and Gradient Compression in Deep Learning Jimmy J. Lin 113 0 0 01 Nov 2022
Joint Privacy Enhancement and Quantization in Federated LearningIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2022 Natalie Lang Elad Sofer Tomer Shaked Stefano Rini FedML 184 64 0 23 Aug 2022
Reconciling Security and Communication Efficiency in Federated LearningIEEE Data Engineering Bulletin (DEB), 2022 Karthik Prasad Sayan Ghosh Graham Cormode Ilya Mironov Ashkan Yousefpour Pierre Stock FedML 144 11 0 26 Jul 2022