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Analyzing data owned by several parties while achieving a good trade-off between utility and privacy is a key challenge in federated learning and analytics.
Differentially Private Summation with Multi-Message Shuffling
Balle, B., Bell, J., Gascón, A., and Nissim, K. (2019a) · 1906
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Pan-private uniformity testing
Amin, K., Joseph, M., and Mao, J. (2019) · 1911
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Advances and Open Problems in Federated Learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konečný, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Raykova, M., Qi, H., Ramage, D., Raskar, R., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S. (2019) · 1912
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Pure Differentially Private Summation from Anonymous Messages
Ghazi, B., Golowich, N., Kumar, R., Manurangsi, P., Pagh, R., and Velingker, A. (2020) · 2002
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Randomized gossip algorithms
Boyd, S., Ghosh, A., Prabhakar, B., and Shah, D. (2006) · 2006
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Sabater, C., Bellet, A., and Ramon, J. (2020) · 2006
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What Can We Learn Privately?
Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., and Smith, A. D. (2008) · 2008
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A(DP)2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy
Xu, J., Zhang, W., and Wang, F. (2020) · 2008
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Private Weighted Random Walk Stochastic Gradient Descent
Ayache, G. and Rouayheb, S. E. (2020) · 2009
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A randomized incremental subgradient method for distributed optimization in networked systems
Johansson, B., Rabi, M., and Johansson, M. (2009) · 2009
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Incremental stochastic subgradient algorithms for convex optimization
Ram, S., Nedić, A., and Veeravalli, V. (2009) · 2009
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Privacy-Preserving Aggregation of Time-Series Data
Shi, E., Chan, T.-H. H., Rieffel, E. G., Chow, R., and Song, D. (2011) · 2011
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Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
Feldman, V., McMillan, A., and Talwar, K. (2020) · 2012
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Local privacy and statistical minimax rates
Duchi, J. C., Jordan, M. I., and Wainwright, M. J. (2013) · 2013
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Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Shamir, O. and Zhang, T. (2013) · 2013
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Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
Bassily, R., Smith, A. D., and Thakurta, A. (2014) · 2014
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Extremal mechanisms for local differential privacy
Kairouz, P., Oh, S., and Viswanath, P. (2014) · 2014
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Differentially Private Distributed Optimization
Huang, Z., Mitra, S., and Vaidya, N. (2015) · 2015
Cited alongside, same era.
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Colin, I., Bellet, A., Salmon, J., and Clémençon, S. (2016) · 2016
Cited alongside, same era.
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K. (2017) · 2017
Cited alongside, same era.
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu, J. (2017) · 2017
Cited alongside, same era.
Rényi Differential Privacy
Mironov, I. (2017) · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited
Wang, D., Gaboardi, M., and Xu, J. (2018) · 2018
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Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Zhang, X., Khalili, M. M., and Liu, M. (2018) · 2018
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Towards Decentralized Deep Learning with Differential Privacy
Cheng, H.-P., Yu, P., Hu, H., Zawad, S., Yan, F., Li, S., Li, H. H., and Chen, Y. (2019) · 2019
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Distributed Differential Privacy via Shuffling
Cheu, A., Smith, A. D., Ullman, J., Zeber, D., and Zhilyaev, M. (2019) · 2019
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Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Erlingsson, U., Feldman, V., Mironov, I., Raghunathan, A., and Talwar, K. (2019) · 2019
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Shokri, R., Stronati, M., Song, C., and Shmatikov, V. (2017) · 2017
Cited alongside, same era.
Decentralized Collaborative Learning of Personalized Models over Networks
Vanhaesebrouck, P., Bellet, A., and Tommasi, M. (2017) · 2017
Cited alongside, same era.
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible
Zheng, K., Mou, W., and Wang, L. (2017) · 2017
Cited alongside, same era.
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Balle, B., Barthe, G., and Gaboardi, M. (2018) · 2018
Cited alongside, same era.
Personalized and Private Peer-to-Peer Machine Learning
Bellet, A., Guerraoui, R., Taziki, M., and Tommasi, M. (2018) · 2018
Cited alongside, same era.
Privacy Amplification by Iteration
Feldman, V., Mironov, I., Talwar, K., and Thakurta, A. (2018) · 2018
Cited alongside, same era.
Distributed learning without distress: Privacy-preserving empirical risk minimization
Jayaraman, B., Wang, L., Evans, D., and Gu, Q. (2018) · 2018
Cited alongside, same era.
Nasr, M., Shokri, R., and Houmansadr, A. (2019) · 2019
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The role of network topology for distributed machine learning
Neglia, G., Calbi, G., Towsley, D., and Vardoyan, G. (2019) · 2019
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Connecting robust shuffle privacy and pan-privacy
Balcer, V., Cheu, A., Joseph, M., and Mao, J. (2020) · 2020
Closest in time.
Privacy Amplification via Random Check-Ins
Balle, B., Kairouz, P., McMahan, B., Thakkar, O. D., and Thakurta, A. (2020) · 2020
Closest in time.
Secure single-server aggregation with (poly)logarithmic overheads
Bell, J. H., Bonawitz, K. A., Gascón, A., Lepoint, T., and Raykova, M. (2020) · 2020
Closest in time.
Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols
Bellet, A., Guerraoui, R., and Hendrikx, H. (2020) · 2020
Closest in time.
Inverting gradients - how easy is it to break privacy in federated learning?
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M. (2020) · 2020
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A unified theory of decentralized sgd with changing topology and local updates
Koloskova, A., Loizou, N., Boreiri, S., Jaggi, M., and Stich, S. U. (2020) · 2020
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Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization
Mao, X., Yuan, K., Hu, Y., Gu, Y., Sayed, A. H., and Yin, W. (2020) · 2020
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Throughput-Optimal Topology Design for Cross-Silo Federated Learning
Marfoq, O., Xu, C., Neglia, G., and Vidal, R. (2020) · 2020
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Decentralized gradient methods: does topology matter?
Neglia, G., Xu, C., Towsley, D., and Calbi, G. (2020) · 2020
Closest in time.