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Federated Learning (FL) allows multiple clients to collaboratively train a Neural Network (NN) model on their private data without revealing the data.
S. Lloyd, “Least squares quantization in pcm,” IEEE transactions on information theory , vol. 28, no. 2, 1982
1982
Earlier work this paper cites.
S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis,” in Chemometrics and intelligent laboratory systems , vol. 2. Elsevier, 1987
1987
Earlier work this paper cites.
S. Chopra, R. Hadsell, and Y. LeCun, “Learning a similarity metric discriminatively, with application to face verification,” in Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2005
2005
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , vol. 22, no. 10, 2009
2009
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine Learning in Python ,” Journal of Machine Learning Research , 2011
2011
Earlier work this paper cites.
R. Shokri and V. Shmatikov, “Privacy-Preserving Deep Learning,” in CCS , 2015
2015
Earlier work this paper cites.
S. Shen, S. Tople, and P. Saxena, “Auror: Defending Against Poisoning Attacks in Collaborative Deep Learning Systems,” in Annual Computer Security Applications Conference (ACSAC) , 2016
2016
Earlier work this paper cites.
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent,” in Advances in Neural Information Processing Systems (NIPS) , 2017
2017
Earlier work this paper cites.
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical Secure Aggregation for Privacy-Preserving Machine Learning,” in CCS , 2017
2017
Earlier work this paper cites.
L. McInnes, J. Healy, and S. Astels, “hdbscan: Hierarchical density based clustering,” The Journal of Open Source Software , 2017
2017
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in International Conference on Artificial Intelligence and Statistics (AISTATS) , 2017
2017
Earlier work this paper cites.
B. McMahan and D. Ramage, “Federated learning: Collaborative Machine Learning without Centralized Training Data,” in Google Research Blog . Google AI, 2017, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
2017
Earlier work this paper cites.
R. Guerraoui, S. Rouault et al. , “The hidden vulnerability of distributed learning in byzantium,” in International Conference on Machine Learning (ICML) , 2018
2018
Earlier work this paper cites.
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, “Trojaning attack on neural networks,” in NDSS , 2018
2018
Earlier work this paper cites.
B. McMahan, D. Ramage, K. Talwar, and L. Zhang, “Learning differentially private recurrent language models,” in International Conference on Learning Representations (ICLR) , 2018
2018
Cited alongside, same era.
M. Sheller, A. Reina, B. Edwards, J. Martin, and S. Bakas, “Federated Learning for Medical Imaging,” in Intel AI , 2018, https://www.intel.com/content/www/us/en/artificial-intelligence/posts/federated-learning-for-medical-imaging.html
2018
Cited alongside, same era.
A. Sivanathan, H. H. Gharakheili, F. Loi, A. Radford, C. Wijenayake, A. Vishwanath, and V. Sivaraman, “Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics,” in IEEE Transactions on Mobile Computing , 2018
2018
Cited alongside, same era.
D. Yin, Y. Chen, R. Kannan, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in International Conference on Machine Learning (ICML) , 2018
2018
Cited alongside, same era.
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” in International Conference on Artificial Intelligence and Statistics (AISTATS) . PMLR, 2020
2020
Later among the works it cites.
M. Fang, X. Cao, J. Jia, and N. Zhenqiang Gong, “Local Model Poisoning Attacks to Byzantine-Robust Federated Learning,” in USENIX Security , 2020
2020
Later among the works it cites.
C. Fung, C. J. Yoon, and I. Beschastnikh, “The limitations of federated learning in sybil settings,” in International Symposium on Research in Attacks, Intrusions and Defenses (RAID) , 2020
2020
Later among the works it cites.
Y. Khazbak, T. Tan, and G. Cao, “Mlguard: Mitigating poisoning attacks in privacy preserving distributed collaborative learning,” in International Conference on Computer Communications and Networks (ICCCN) . IEEE, 2020
2020
Later among the works it cites.
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M. Baruch, G. Baruch, and Y. Goldberg, “A Little Is Enough: Circumventing Defenses For Distributed Learning,” in Advances in Neural Information Processing Systems (NIPS) , 2019
2019
Cited alongside, same era.
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro, “Logan: Membership inference attacks against generative models,” in Privacy Enhancing Technologies , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Y. Liu, W.-C. Lee, G. Tao, S. Ma, Y. Aafer, and X. Zhang, “Abs: Scanning neural networks for back-doors by artificial brain stimulation,” in ACM SIGSAC Conference on Computer and Communications Security , 2019
2019
Cited alongside, same era.
L. Muñoz-González, K. T. Co, and E. C. Lupu, “Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging,” in arXiv preprint:1909.05125 , 2019
2019
Cited alongside, same era.
M. Nasr, R. Shokri, and A. Houmansadr, “Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,” in S&P . IEEE, 2019
2019
Cited alongside, same era.
T. D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, and A. Sadeghi, “DÏoT: A Federated Self-learning Anomaly Detection System for IoT,” in ICDCS , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Later among the works it cites.
T. D. Nguyen, P. Rieger, M. Miettinen, and A.-R. Sadeghi, “Poisoning Attacks on Federated Learning-Based IoT Intrusion Detection System,” in Workshop on Decentralized IoT Systems and Security (DISS) @ NDSS , 2020
2020
Later among the works it cites.
L. Rieger, R. M. T. Høegh, and L. K. Hansen, “Client adaptation improves federated learning with simulated non-iid clients,” in International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 . International Machine Learning Society (IMLS), 2020
2020
Later among the works it cites.
A. Salem, A. Bhattacharya, M. Backes, M. Fritz, and Y. Zhang, “Updates-leak: Data set inference and reconstruction attacks in online learning,” in USENIX Security , 2020
2020
Later among the works it cites.
H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J.-y. Sohn, K. Lee, and D. Papailiopoulos, “Attack of the tails: Yes, you really can backdoor federated learning,” in NeurIPS , 2020
2020
Later among the works it cites.
S. Andreina, G. A. Marson, H. Möllering, and G. Karame, “BaFFLe: Backdoor Detection via Feedback-based Federated Learning,” in ICDCS , 2021
2021
Later among the works it cites.
X. Cao, J. Jia, and N. Z. Gong, “Provably secure federated learning against malicious clients,” AAAI Conference on Artificial Intelligence , 2021
2021
Later among the works it cites.
H. Fereidooni, S. Marchal, M. Miettinen, A. Mirhoseini, H. Möllering, T. D. Nguyen, P. Rieger, A.-R. Sadeghi, T. Schneider, H. Yalame, and S. Zeitouni, “SAFELearn: secure aggregation for private federated learning,” in IEEE Security and Privacy Workshops (SPW) . IEEE, 2021
2021
Later among the works it cites.