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Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data.
How to share a secret
Shamir, A · 1979
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Paillier, P · 1999
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Improved proxy re-encryption schemes with applications to secure distributed storage
Ateniese, G., Fu, K., Green, M., and Hohenberger, S · 2006
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Chosen ciphertext secure public key threshold encryption without random oracles
Boneh, D., Boyen, X., and Halevi, S · 2006
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Differential privacy: A survey of results
Dwork, C · 2008
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Laud, P. and Ngo, L · 2008
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Gentry, C · 2009
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Helayers: A tile tensors framework for large neural networks on encrypted data, 2011
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Multiparty computation with low communication, computation and interaction via threshold fhe
Asharov, G., Jain, A., López-Alt, A., Tromer, E., Vaikuntanathan, V., and Wichs, D · 2012
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Somewhat practical fully homomorphic encryption
Fan, J. and Vercauteren, F · 2012
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(leveled) fully homomorphic encryption without bootstrapping
Brakerski, Z., Gentry, C., and Vaikuntanathan, V · 2014
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Kcofi: Complete control-flow integrity for commodity operating system kernels
Criswell, J., Dautenhahn, N., and Adve, V · 2014
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Privacy-preserving deep learning
Shokri, R. and Shmatikov, V · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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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
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Homomorphic encryption for arithmetic of approximate numbers
Cheon, J. H., Kim, A., Kim, M., and Song, Y · 2017
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Deep models under the gan: information leakage from collaborative deep learning
Hitaj, B., Ateniese, G., and Perez-Cruz, F · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Robust large margin deep neural networks
Sokolić, J., Giryes, R., Sapiro, G., and Rodrigues, M. R · 2017
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Zhu, L., Liu, Z., and Han, S · 2017
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Protection against reconstruction and its applications in private federated learning
Bhowmick, A., Duchi, J., Freudiger, J., Kapoor, G., and Rogers, R · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Sensitivity and generalization in neural networks: an empirical study
Novak, R., Bahri, Y., Abolafia, D. A., Pennington, J., and Sohl-Dickstein, J · 2018
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Flashe: Additively symmetric homomorphic encryption for cross-silo federated learning
Jiang, Z., Wang, W., and Liu, Y · 2021
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A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., and Srivastava, G · 2021
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Secure neuroimaging analysis using federated learning with homomorphic encryption
Stripelis, D., Saleem, H., Ghai, T., Dhinagar, N., Gupta, U., Anastasiou, C., Ver Steeg, G., Ravi, S., Naveed, M., Thompson, P. M., et al · 2021
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Decepticons: Corrupted transformers breach privacy in federated learning for language models
Fowl, L., Geiping, J., Reich, S., Wen, Y., Czaja, W., Goldblum, M., and Goldstein, T · 2022
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New insights into fully homomorphic encryption libraries via standardized benchmarks
Gouert, C., Mouris, D., and Tsoutsos, N. G · 2022
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Nasr, M., Shokri, R., and Houmansadr, A · 2019
Cited alongside, same era.
Doublesqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression
Tang, H., Yu, C., Lian, X., Zhang, T., and Liu, J · 2019
Cited alongside, same era.
Beyond inferring class representatives: User-level privacy leakage from federated learning
Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., and Qi, H · 2019
Cited alongside, same era.
Differentially private secure multi-party computation for federated learning in financial applications
Byrd, D. and Polychroniadou, A · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2020
Cited alongside, same era.
Layer-wise characterization of latent information leakage in federated learning
Mo, F., Borovykh, A., Malekzadeh, M., Haddadi, H., and Demetriou, S · 2020
Cited alongside, same era.
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Gradvit: Gradient inversion of vision transformers
Hatamizadeh, A., Yin, H., Roth, H. R., Li, W., Kautz, J., Xu, D., and Molchanov, P · 2022
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Secure publish-process-subscribe system for dispersed computing
Jin, W., Krishnamachari, B., Naveed, M., Ravi, S., Sanou, E., and Wright, K.-L · 2022
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April: Finding the achilles’ heel on privacy for vision transformers
Lu, J., Zhang, X. S., Zhao, T., He, X., and Cheng, J · 2022
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Privacy-preserving federated learning based on multi-key homomorphic encryption
Ma, J., Naas, S.-A., Sigg, S., and Lyu, X · 2022
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Nvidia flare: Federated learning from simulation to real-world
Roth, H. R., Cheng, Y., Wen, Y., Yang, I., Xu, Z., Hsieh, Y.-T., Kersten, K., Harouni, A., Zhao, C., Lu, K., et al · 2022
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Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning
So, J., Nolet, C. J., Yang, C.-S., Li, S., Yu, Q., E Ali, R., Guler, B., and Avestimehr, S · 2022
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On the unreasonable effectiveness of federated averaging with heterogeneous data
Wang, J., Das, R., Joshi, G., Kale, S., Xu, Z., and Zhang, T · 2022
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A efficient and robust privacy-preserving framework for cross-device federated learning
Du, W., Li, M., Wu, L., Han, Y., Zhou, T., and Yang, X · 2023
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Fedmlsecurity: A benchmark for attacks and defenses in federated learning and llms
Han, S., Buyukates, B., Hu, Z., Jin, H., Jin, W., Sun, L., Wang, X., Xie, C., Zhang, K., Zhang, Q., et al · 2023
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Ibmfl crypto
IBM · 2023
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Fedgcn: Convergence and communication tradeoffs in federated training of graph convolutional networks
Yao, Y., Jin, W., Ravi, S., and Joe-Wong, C · 2023
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Demystifying membership inference attacks in machine learning as a service
Truex, S., Liu, L., Gursoy, M. E., Yu, L., and Wei, W · 2089
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