Fetching the paper…
Reading the bibliography…
We propose and implement a Privacy-preserving Federated Learning ($PPFL$) framework for mobile systems to limit privacy leakages in federated learning.
Evaluating differentially private machine learning in practice
Jayaraman, B., and Evans, D · 1912
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Sigma: The ‘sign-and-mac’approach to authenticated diffie-hellman and its use in the ike protocols
Krawczyk, H · 2003
Earlier work this paper cites.
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Exploring strategies for training deep neural networks
Larochelle, H., Bengio, Y., Louradour, J., and Lamblin, P · 2009
Earlier work this paper cites.
A survey on transfer learning
Pan, S. J., and Yang, Q · 2009
Earlier work this paper cites.
Transfer learning
Torrey, L., and Shavlik, J · 2010
Earlier work this paper cites.
Can homomorphic encryption be practical?
Naehrig, M., Lauter, K., and Vaikuntanathan, V · 2011
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
Earlier work this paper cites.
Modelling and automatically analysing privacy properties for honest-but-curious adversaries
Paverd, A., Martin, A., and Brown, I · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., and Zisserman, A · 2014
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Earlier work this paper cites.
Vc3: Trustworthy data analytics in the cloud using sgx
Schuster, F., Costa, M., Fournet, C., Gkantsidis, C., Peinado, M., Mainar-Ruiz, G., and Russinovich, M · 2015
Earlier work this paper cites.
Revisiting distributed synchronous sgd
Chen, J., Pan, X., Monga, R., Bengio, S., and Jozefowicz, R · 2016
Earlier work this paper cites.
Intel sgx explained
Costan, V., and Devadas, S · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Oblivious multi-party machine learning on trusted processors
Ohrimenko, O., Schuster, F., Fournet, C., Mehta, A., Nowozin, S., Vaswani, K., and Costa, M · 2016
Earlier work this paper cites.
Darknet: Open source neural networks in c
Redmon, J · 2016
Earlier work this paper cites.
Privacy-preserving deep learning via additively homomorphic encryption
Aono, Y., Hayashi, T., Wang, L., Moriai, S., et al · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Differentially private federated learning: A client level perspective
Geyer, R. C., Klein, T., and Nabi, M · 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.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2017
Earlier work this paper cites.
Hacking in Darkness: Return-Oriented Programming against Secure Enclaves
Lee, J., Jang, J., Jang, Y., Kwak, N., Choi, Y., Choi, C., Kim, T., Peinado, M., and Kang, B. B · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Cited alongside, same era.
Yerbabuena: Securing deep learning inference data via enclave-based ternary model partitioning
Gu, Z., Huang, H., Zhang, J., Su, D., Jamjoom, H., Lamba, A., Pendarakis, D., and Molloy, I · 2018
Cited alongside, same era.
Chiron: Privacy-preserving machine learning as a service
Hunt, T., Song, C., Shokri, R., Shmatikov, V., and Witchel, E · 2018
Cited alongside, same era.
Integrating remote attestation with transport layer security
Knauth, T., Steiner, M., Chakrabarti, S., Lei, L., Xing, C., and Vij, M · 2018
Cited alongside, same era.
Sectee: A software-based approach to secure enclave architecture using tee
Zhao, S., Zhang, Q., Qin, Y., Feng, W., and Feng, D · 2019
Later among the works it cites.
Zhu, L., Liu, Z., and Han, S · 2019
Later among the works it cites.
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V · 2020
Later among the works it cites.
A Gentle Introduction to Transfer Learning for Deep Learning
Brownlee, J · 2020
Later among the works it cites.
Local model poisoning attacks to byzantine-robust federated learning
Fang, M., Cao, X., Jia, J., and Gong, N · 2020
Later among the works it cites.
Inverting gradients–how easy is it to break privacy in federated learning?
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2018
Cited alongside, same era.
Trust Anchors in Software Defined Networks
Paladi, N., Karlsson, L., and Elbashir, K · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 2018
Cited alongside, same era.
On the performance of arm trustzone
Amacher, J., and Schiavoni, V · 2019
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Bagdasaryan, E., Poursaeed, O., and Shmatikov, V · 2019
Cited alongside, same era.
Greedy layerwise learning can scale to imagenet
Belilovsky, E., Eickenberg, M., and Oyallon, E · 2019
Cited alongside, same era.
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M · 2020
Later among the works it cites.
An efficiency-boosting client selection scheme for federated learning with fairness guarantee
Huang, T., Lin, W., Wu, W., He, L., Li, K., and Zomaya, A. Y · 2020
Later among the works it cites.
Telekine: Secure computing with cloud gpus
Hunt, T., Jia, Z., Miller, V., Szekely, A., Hu, Y., Rossbach, C. J., and Witchel, E · 2020
Later among the works it cites.
A taxonomy of attacks on federated learning
Jere, M. S., Farnan, T., and Koushanfar, F · 2020
Later among the works it cites.
Flaas: Federated learning as a service
Kourtellis, N., Katevas, K., and Perino, D · 2020
Later among the works it cites.
Open Portable Trusted Execution Environment
Linaro.org · 2020
Later among the works it cites.
A secure federated transfer learning framework
Liu, Y., Kang, Y., Xing, C., Chen, T., and Yang, Q · 2020
Later among the works it cites.
Open Enclave SDK
Microsoft · 2020
Later among the works it cites.
Darknetz: towards model privacy at the edge using trusted execution environments
Mo, F., Shamsabadi, A. S., Katevas, K., Demetriou, S., Leontiadis, I., Cavallaro, A., and Haddadi, H · 2020
Later among the works it cites.
Monsoon solutions inc. home page
Monsoon · 2020
Later among the works it cites.
https://github.com/Microsoft/SEAL , Apr. 2020
Microsoft SEAL (release 3.5) · 2020
Later among the works it cites.
Enabling fast differentially private sgd via just-in-time compilation and vectorization
Subramani, P., Vadivelu, N., and Kamath, G · 2020
Later among the works it cites.
Federated learning with matched averaging
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y · 2020
Later among the works it cites.
L2-gcn: Layer-wise and learned efficient training of graph convolutional networks
You, Y., Chen, T., Wang, Z., and Shen, Y · 2020
Later among the works it cites.
Enabling execution assurance of federated learning at untrusted participants
Zhang, X., Li, F., Zhang, Z., Li, Q., Wang, C., and Wu, J · 2020
Later among the works it cites.
Not one but many tradeoffs: Privacy vs. utility in differentially private machine learning
Zhao, B. Z. H., Kaafar, M. A., and Kourtellis, N · 2020
Later among the works it cites.
Voltpillager: Hardware-based fault injection attacks against intel SGX enclaves using the SVID voltage scaling interface
Chen, Z., Vasilakis, G., Murdock, K., Dean, E., Oswald, D., and Garcia, F. D · 2021
Closest in time.
Policy-based federated learning
Katevas, K., Bagdasaryan, E., Waterman, J., Safadieh, M. M., Birrell, E., Haddadi, H., and Estrin, D · 2021
Closest in time.
PLATYPUS: Software-based Power Side-Channel Attacks on x86
Lipp, M., Kogler, A., Oswald, D., Schwarz, M., Easdon, C., Canella, C., and Gruss, D · 2021
Closest in time.
Layer-wise characterization of latent information leakage in federated learning
Mo, F., Borovykh, A., Malekzadeh, M., Haddadi, H., and Demetriou, S · 2021
Closest in time.
Introducing Opacus: A high-speed library for training PyTorch models with differential privacy
Testuggine, D., and Mironov, I · 2021
Closest in time.