Fetching the paper…
Reading the bibliography…
We show that aggregated model updates in federated learning may be insecure.
Towards federated learning at scale: System design
Bonawitz, K. A., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C. M., Konečný, J., Mazzocchi, S., McMahan, B., Overveldt, T. V., Petrou, D., Ramage, D., and Roselander, J · 1902
Earlier work this paper cites.
Labeled faces in the wild: A database for studying face recognition in unconstrained environments
Huang, G. B., Ramesh, M., Berg, T., and Learned-Miller, E · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
Just you and me and netflix makes three: Implications for allowing ”frictionless sharing” of personally identifiable information under the video privacy protection act
McCabe, K. E · 2013
Earlier work this paper cites.
Matrix factorization with binary components
Slawski, M., Hein, M., and Lutsik, P · 2013
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
Earlier work this paper cites.
Federated optimization:distributed optimization beyond the datacenter, 2015
Konečný, J., McMahan, B., and Ramage, D · 2015
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Han, S., Mao, H., and Dally, W. J · 2016
Earlier work this paper cites.
Machine learning with adversaries: Byzantine tolerant gradient descent
Blanchard, P., El Mhamdi, E. M., Guerraoui, R., and Stainer, J · 2017
Earlier work this paper cites.
Deep models under the gan: Information leakage from collaborative deep learning
Hitaj, B., Ateniese, G., and Perez-Cruz, F · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications, 2017
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency, 2017
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2017
Earlier work this paper cites.
Federated learning google
McMahan, B. and Ramage, D · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data, 2017
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Cited alongside, same era.
Practical secure aggregation for privacy-preserving machine learning
Segal, A., Marcedone, A., Kreuter, B., Ramage, D., McMahan, H. B., Seth, K., Bonawitz, K. A., Patel, S., and Ivanov, V · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models, 2017
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Cited alongside, same era.
I know what you bought at chipotle for $9.81 by solving a linear inverse problem
Fleder, M. and Shah, D · 2020
Later among the works it cites.
Mitigating sybils in federated learning poisoning, 2020
Fung, C., Yoon, C. J. M., and Beschastnikh, I · 2020
Later among the works it cites.
Inverting gradients – how easy is it to break privacy in federated learning?, 2020
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M · 2020
Later among the works it cites.
Gurobi optimizer reference manual, 2020
Gurobi Optimization, L · 2020
Later among the works it cites.
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2020
Later among the works it cites.
Threats to federated learning: A survey, 2020
Lyu, L., Yu, H., and Yang, Q · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hard, A., Kiddon, C. M., Ramage, D., Beaufays, F., Eichner, H., Rao, K., Mathews, R., and Augenstein, S · 2018
Cited alongside, same era.
How to backdoor federated learning, 2019
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V · 2019
Cited alongside, same era.
Ftc privacy restrictions facebook
FTC · 2019
Cited alongside, same era.
Advances and open problems in federated learning, 2019
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
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V · 2019
Cited alongside, same era.
Deep leakage from gradients, 2019
Zhu, L., Liu, Z., and Han, S · 2019
Cited alongside, same era.
Analyzing user-level privacy attack against federated learning
Mengkai, S., Wang, Z., Zhang, Z., Song, Y., Wang, Q., Ren, J., and Qi, H · 2020
Later among the works it cites.
What can we learn from gradients?, 2020
Qian, J. and Hansen, L. K · 2020
Later among the works it cites.
Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning, 2020
So, J., Guler, B., and Avestimehr, A. S · 2020
Later among the works it cites.
Attack of the tails: Yes, you really can backdoor federated learning, 2020
Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., yong Sohn, J., Lee, K., and Papailiopoulos, D · 2020
Later among the works it cites.
A framework for evaluating gradient leakage attacks in federated learning, 2020
Wei, W., Liu, L., Loper, M., Chow, K.-H., Gursoy, M. E., Truex, S., and Wu, Y · 2020
Later among the works it cites.