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We consider a distributed empirical risk minimization (ERM) optimization problem with communication efficiency and privacy requirements, motivated by the federated learning (FL) framework.
Randomized response: A survey technique for eliminating evasive answer bias
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Cynthia Dwork and Aaron Roth · 2014
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Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Ananda Theertha Suresh, Felix X Yu, Sanjiv Kumar, and H Brendan McMahan · 2017
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Lower bounds for locally private estimation via communication complexity
John C. Duchi and Ryan Rogers · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019
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Venkata Gandikota, Daniel Kane, Raj Kumar Maity, and Arya Mazumdar · 2019
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Scalable and differentially private distributed aggregation in the shuffled model
Badih Ghazi, Rasmus Pagh, and Ameya Velingker · 2019
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Minimax optimal procedures for locally private estimation
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2018
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Sparsified sgd with memory
Sebastian U Stich, Jean-Baptiste Cordonnier, and Martin Jaggi · 2018
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Communication complexity in locally private distribution estimation and heavy hitters
Jayadev Acharya and Ziteng Sun · 2019
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Hadamard response: Estimating distributions privately, efficiently, and with little communication
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Differentially private summation with multi-message shuffling
Borja Balle, James Bell, Adria Gascon, and Kobbi Nissim · 2019
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Decentralized deep learning with arbitrary communication compression
Anastasia Koloskova, Tao Lin, Sebastian U Stich, and Martin Jaggi · 2019
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Navjot Singh, Deepesh Data, Jemin George, and Suhas Diggavi · 2019
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Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Private summation in the multi-message shuffle model
Borja Balle, James Bell, Adria Gascon, and Kobbi Nissim · 2020
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