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This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1986 · 1986
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. 1989 · 1989
Earlier work this paper cites.
Design patterns: elements of reusable object-oriented software
Erich Gamma, Richard Helm, Ralph Johnson, Ralph E Johnson, and John Vlissides. 1995 · 1995
Earlier work this paper cites.
NewsWeeder: Learning to Filter Netnews
Ken Lang. 1995 · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998 · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Secret-sharing schemes: A survey. In International conference on coding and cryptology . Springer, 11–46
Amos Beimel. 2011 · 2011
Earlier work this paper cites.
Synchronous Parallel Processing of Big-Data Analytics Services to Optimize Performance in Federated Clouds
Gueyoung Jung, Nathan Gnanasambandam, and Tridib Mukherjee. 2012 · 2012
Earlier work this paper cites.
Generative Adversarial Nets. In NIPS
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015 · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. 2015 · 2015
Earlier work this paper cites.
Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security . 1310–1321
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data. In International Conference on Artificial Intelligence and Statistics
H. B. McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2016 · 2016
Earlier work this paper cites.
Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017 · 2017
Earlier work this paper cites.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Y. Chen, L. Su, and J. Xu. 2017 · 2017
Earlier work this paper cites.
Deep models under the GAN: information leakage from collaborative deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security . 603–618
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz. 2017 · 2017
Earlier work this paper cites.
Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
The hidden vulnerability of distributed learning in byzantium. In International Conference on Machine Learning . PMLR, 3521–3530
Rachid Guerraoui, Sébastien Rouault, et al · 2018
Earlier work this paper cites.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018 · 2018
Earlier work this paper cites.
Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2018 · 2018
Earlier work this paper cites.
Byzantine-robust distributed learning: Towards optimal statistical rates. In International Conference on Machine Learning . PMLR, 5650–5659
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett. 2018 · 2018
Earlier work this paper cites.
A little is enough: Circumventing defenses for distributed learning
Gilad Baruch, Moran Baruch, and Yoav Goldberg. 2019 · 2019
Earlier work this paper cites.
Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning . PMLR, 634–643
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019 · 2019
Earlier work this paper cites.
Federated learning of out-of-vocabulary words
Mingqing Chen, Rajiv Mathews, Tom Ouyang, and Françoise Beaufays. 2019 · 2019
Earlier work this paper cites.
Attack-resistant federated learning with residual-based reweighting
Shuhao Fu, Chulin Xie, Bo Li, and Qifeng Chen. 2019 · 2019
Earlier work this paper cites.
Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2019 · 2019
Earlier work this paper cites.
PubMedQA: A Dataset for Biomedical Research Question Answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 2567–2577
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019 · 2019
Earlier work this paper cites.
Federated learning for keyword spotting. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . 6341–6345
David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau. 2019 · 2019
Earlier work this paper cites.
RSA: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 1544–1551
Liping Li, Wei Xu, Tianyi Chen, Georgios B Giannakis, and Qing Ling. 2019 · 2019
Earlier work this paper cites.
Free-riders in federated learning: Attacks and defenses
Jierui Lin, Min Du, and Jian Liu. 2019 · 2019
Earlier work this paper cites.
Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE symposium on security and privacy (SP) . IEEE, 691–706
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
Earlier work this paper cites.
Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays. 2019 · 2019
Cited alongside, same era.
Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. 2019 · 2019
Cited alongside, same era.
Byzantine-resilient stochastic gradient descent for distributed learning: A Lipschitz-inspired coordinate-wise median approach. In IEEE CDC
H. Yang, X. Zhang, M. Fang, and J. Liu. Dec 2019 · 2019
Cited alongside, same era.
Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han. 2019 · 2019
Cited alongside, same era.
How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics . PMLR, 2938–2948
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020 · 2020
Adaptive Federated Optimization. In International Conference on Learning Representations
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan. 2021 · 2021
Later among the works it cites.
OpenFL: An open-source framework for Federated Learning
G Anthony Reina, Alexey Gruzdev, Patrick Foley, Olga Perepelkina, Mansi Sharma, Igor Davidyuk, Ilya Trushkin, Maksim Radionov, Aleksandr Mokrov, Dmitry Agapov, et al · 2021
Later among the works it cites.
Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning. In NDSS
Virat Shejwalkar and Amir Houmansadr. 2021 · 2021
Later among the works it cites.
Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective
Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, and Hai Li. 2021 · 2021
Later among the works it cites.
CRFL: Certifiably robust federated learning against backdoor attacks. In International Conference on Machine Learning . PMLR, 11372–11382
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Cited alongside, same era.
Flower: A friendly federated learning research framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, Pedro PB de Gusmão, and Nicholas D Lane. 2020 · 2020
Cited alongside, same era.
Differentially private secure multi-party computation for federated learning in financial applications. In Proceedings of the First ACM International Conference on AI in Finance . 1–9
David Byrd and Antigoni Polychroniadou. 2020 · 2020
Cited alongside, same era.
Local model poisoning attacks to { \{ Byzantine-Robust } \} federated learning. In 29th USENIX security symposium (USENIX Security 20) . 1605–1622
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong. 2020 · 2020
Cited alongside, same era.
The Limitations of Federated Learning in Sybil Settings.. In RAID . 301–316
Clement Fung, Chris JM Yoon, and Ivan Beschastnikh. 2020 · 2020
Cited alongside, same era.
Inverting gradients-how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller. 2020 · 2020
Cited alongside, same era.
Group knowledge transfer: Federated learning of large cnns at the edge
Chaoyang He, Murali Annavaram, and Salman Avestimehr. 2020a · 2020
Cited alongside, same era.
FedML: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, et al · 2020
Cited alongside, same era.
Chulin Xie, Minghao Chen, Pin-Yu Chen, and Bo Li. 2021 · 2021
Later among the works it cites.
PySyft: A library for easy federated learning
Alexander Ziller, Andrew Trask, Antonio Lopardo, Benjamin Szymkow, Bobby Wagner, Emma Bluemke, Jean-Mickael Nounahon, Jonathan Passerat-Palmbach, Kritika Prakash, Nick Rose, et al · 2021
Later among the works it cites.
FedDef: Defense Against Gradient Leakage in Federated Learning-Based Network Intrusion Detection Systems
Jiahui Chen, Yi Zhao, Qi Li, Xuewei Feng, and Ke Xu. 2022 · 2022
Later among the works it cites.
Flute: A scalable, extensible framework for high-performance federated learning simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal Diaz, Andre Manoel, and Robert Sim. 2022 · 2022
Later among the works it cites.
FedClean: A Defense Mechanism against Parameter Poisoning Attacks in Federated Learning
Abhishek Kumar, Vivek Khimani, Dimitris Chatzopoulos, and Pan Hui. 2022 · 2022
Later among the works it cites.
FedScale: Benchmarking model and system performance of federated learning at scale. In International Conference on Machine Learning . PMLR, 11814–11827
Fan Lai, Yinwei Dai, Sanjay Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha Madhyastha, and Mosharaf Chowdhury. 2022 · 2022
Later among the works it cites.
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Xingyu Li, Zhe Qu, Shangqing Zhao, Bo Tang, Zhuo Lu, and Yao-Hong Liu. 2022 · 2022
Later among the works it cites.
BioGPT: generative pre-trained transformer for biomedical text generation and mining
Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu. 2022 · 2022
Later among the works it cites.
Privacy and robustness in federated learning: Attacks and defenses
Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, and S Yu Philip. 2022 · 2022
Later among the works it cites.
ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning
Zhuo Ma, Jianfeng Ma, Yinbin Miao, Yingjiu Li, and Robert H. Deng. 2022 · 2022
Later among the works it cites.
Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui. 2022 · 2022
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NVIDIA FLARE: Federated Learning from Simulation to Real-World
Holger R Roth, Yan Cheng, Yuhong Wen, Isaac Yang, Ziyue Xu, Yuan-Ting Hsieh, Kristopher Kersten, Ahmed Harouni, Can Zhao, Kevin Lu, et al · 2022
Later among the works it cites.
Federated Analytics: Opportunities and Challenges
Dan Wang, Siping Shi, Yifei Zhu, and Zhu Han. 2022b · 2022
Later among the works it cites.
PASS: Parameters Audit-based Secure and Fair Federated Learning Scheme against Free Rider
Jianhua Wang. 2022 · 2022
Later among the works it cites.
Poisoning-assisted property inference attack against federated learning
Zhibo Wang, Yuting Huang, Mengkai Song, Libing Wu, Feng Xue, and Kui Ren. 2022a · 2022
Later among the works it cites.
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Yuexiang Xie, Zhen Wang, Daoyuan Chen, Dawei Gao, Liuyi Yao, Weirui Kuang, Yaliang Li, Bolin Ding, and Jingren Zhou. 2022 · 2022
Later among the works it cites.
Byzantine-robust federated learning through collaborative malicious gradient filtering. In 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) . IEEE, 1223–1235
Jian Xu, Shao-Lun Huang, Linqi Song, and Tian Lan. 2022 · 2022
Later among the works it cites.
Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, and Lichao Sun. 2022b · 2022
Later among the works it cites.
Introducing PyTorch Lightning 2.0 and Fabric
Luca Antiga. 2023 · 2023
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Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al · 2023
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Federated Large Language Model : A Position Paper
Chaochao Chen, Xiaohua Feng, Jun Zhou, Jianwei Yin, and Xiaolin Zheng. 2023 · 2023
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Federated analytics: A survey
Ahmed Roushdy Elkordy, Yahya H Ezzeldin, Shanshan Han, Shantanu Sharma, Chaoyang He, Sharad Mehrotra, Salman Avestimehr, et al · 2023
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Releasing FedLLM: Build Your Own Large Language Models on Proprietary Data using the FedML Platform
FedML Inc. 2023 · 2023
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Kick Bad Guys Out! Zero-Knowledge-Proof-Based Anomaly Detection in Federated Learning
Shanshan Han, Wenxuan Wu, Baturalp Buyukates, Weizhao Jin, Yuhang Yao, Qifan Zhang, Salman Avestimehr, and Chaoyang He. 2023 · 2023
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Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning
Hongsheng Hu, Xuyun Zhang, Zoran Salcic, Lichao Sun, Kim-Kwang Raymond Choo, and Gillian Dobbie. 2023 · 2023
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BayBFed: Bayesian Backdoor Defense for Federated Learning
Kavita Kumari, Phillip Rieger, Hossein Fereidooni, Murtuza Jadliwala, and Ahmad-Reza Sadeghi. 2023 · 2023
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Theta Network Website
Theta Network. 2023 · 2023
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ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Guanhua Wang, Heyang Qin, Sam Ade Jacobs, Connor Holmes, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, and Yuxiong He. 2023 · 2023
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Blades: A unified benchmark suite for byzantine attacks and defenses in federated learning. In 2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI)
Shenghui Li, Edith Ngai, Fanghua Ye, Li Ju, Tianru Zhang, and Thiemo Voigt. 2024 · 2024
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