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For model privacy, local model parameters in federated learning shall be obfuscated before sent to the remote aggregator.
New directions in cryptography
Diffie, W., and Hellman, M · 1976
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Probability and statistics for engineers and scientists
Walpole, R. E., Myers, R. H., Myers, S. L., and Ye, K · 1993
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The sybil attack
Douceur, J. R · 2002
Earlier work this paper cites.
Adversarial machine learning
Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., and Tygar, J. D · 2011
Earlier work this paper cites.
Privacy-preserving aggregation of time-series data
Shi, E., Chan, H., Rieffel, E., Chow, R., and Song, D · 2011
Earlier work this paper cites.
I have a dream! (differentially private smart metering)
Ács, G., and Castelluccia, C · 2011
Earlier work this paper cites.
Poisoning attacks against support vector machines
Biggio, B., Nelson, B., and Laskov, P · 2012
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Privacy-preserving stream aggregation with fault tolerance
Chan, T.-H. H., Shi, E., and Song, D · 2012
Earlier work this paper cites.
Privacy-preserving ridge regression on hundreds of millions of records
Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., and Taft, N · 2013
Earlier work this paper cites.
Machine learning classification over encrypted data
Bost, R., Popa, R. A., Tu, S., and Goldwasser, S · 2015
Earlier work this paper cites.
A comprehensive comparison of multiparty secure additions with differential privacy
Goryczka, S., and Xiong, L · 2015
Earlier work this paper cites.
Deep learning with limited numerical precision
Gupta, S., Agrawal, A., Gopalakrishnan, K., and Narayanan, P · 2015
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 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
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V · 2018
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Mitigating sybils in federated learning poisoning
Fung, C., Yoon, C. J., and Beschastnikh, I · 2018
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Gazelle: A low latency framework for secure neural network inference
Juvekar, C., Vaikuntanathan, V., and Chandrakasan, A · 2018
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Aby3: A mixed protocol framework for machine learning
Mohassel, P., and Rindal, P · 2018
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Chameleon: A hybrid secure computation framework for machine learning applications
Riazi, M. S., Weinert, C., Tkachenko, O., Songhori, E. M., Schneider, T., and Koushanfar, F · 2018
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Chen, X., Liu, C., Li, B., Lu, K., and Song, D · 2017
Cited alongside, same era.
Differentially private federated learning: A client level perspective
Geyer, R. C., Klein, T., and Nabi, M · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2017
Cited alongside, same era.
Oblivious neural network predictions via minionn transformations
Liu, J., Juuti, M., Lu, Y., and Asokan, N · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Cited alongside, same era.
Secureml: A system for scalable privacy-preserving machine learning
Mohassel, P., and Zhang, Y · 2017
Cited alongside, same era.
Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P., Guerraoui, R., Stainer, J., et al
Cited in the paper.
Federated learning: Collaborative machine learning without centralized training data
2019
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Under the hood of the pixel 2: How ai is supercharging hardware
2019
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Privacy preserving machine learning: Threats and solutions
Al-Rubaie, M., and Chang, J. M · 2019
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Analyzing federated learning through an adversarial lens
Bhagoji, A. N., Chakraborty, S., Mittal, P., and Calo, S · 2019
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SecureNN: Efficient and private neural network training
Wagh, S., Gupta, D., and Chandran, N · 2019
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Dba: Distributed backdoor attacks against federated learning
Xie, C., Huang, K., Chen, P.-Y., and Li, B · 2019
Later among the works it cites.