This library implements the following attacks and defenses.

Attack

1. ByzantineAttack: (1) zero mode (2) random mode (3) flip mode
2. DLGAttack: "Deep leakage from gradients" https://proceedings.neurips.cc/paper/2019/file/60a6c4002cc7b29142def8871531281a-Paper.pdf
3. InvertAttack: "Inverting gradients-how easy is it to break privacy in federated learning?" https://github.com/JonasGeiping/invertinggradients/
4. LabelFlippingAttack: "Data Poisoning Attacks Against Federated Learning Systems" https://arxiv.org/pdf/2007.08432
5. RevealingLabelsFromGradientsAttack: "Revealing and Protecting Labels in Distributed Training" https://proceedings.neurips.cc/paper/2021/file/0d924f0e6b3fd0d91074c22727a53966-Paper.pdf
6. BackdoorAttack: "A Little Is Enough: Circumventing Defenses For Distributed Learning" https://proceedings.neurips.cc/paper/2019/file/ec1c59141046cd1866bbbcdfb6ae31d4-Paper.pdf
7. EdgeCaseBackdoorAttack: "Attack of the Tails: Yes, You Really Can Backdoor Federated Learning" https://proceedings.neurips.cc/paper/2020/file/b8ffa41d4e492f0fad2f13e29e1762eb-Paper.pdf
8. ModelReplacementBackdoorAttack: "How To Backdoor Federated Learning" http://proceedings.mlr.press/v108/bagdasaryan20a/bagdasaryan20a.pdf

Defense

1. BulyanDefense: "The Hidden Vulnerability of Distributed Learning in Byzantium. " http://proceedings.mlr.press/v80/mhamdi18a/mhamdi18a.pdf
2. CClipDefense: "Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing" https://arxiv.org/pdf/2006.09365.pdf
3. GeometricMedianDefense: "Distributed statistical machine learning in adversarial settings: Byzantine gradient descent. " https://dl.acm.org/doi/pdf/10.1145/3154503
4. KrumDefense: "Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent" https://papers.nips.cc/paper/2017/file/f4b9ec30ad9f68f89b29639786cb62ef-Paper.pdf
5. MultiKrumDefense: "Distributed momentum for byzantine-resilient stochastic gradient descent" https://infoscience.epfl.ch/record/287261
6. NormDiffClippingDefense: "Can You Really Backdoor Federated Learning?" https://arxiv.org/pdf/1911.07963.pdf
7. RobustLearningRateDefense: "Defending against backdoors in federated learning with robust learning rate." https://github.com/TinfoilHat0/Defending-Against-Backdoors-with-Robust-Learning-Rate
8. SoteriaDefense: "Provable defense against privacy leakage in federated learning from representation perspective." https://arxiv.org/pdf/2012.06043
9. SLSGDDefense: "SLSGD: Secure and efficient distributed on-device machine learning" https://arxiv.org/pdf/1903.06996.pdf
10. RFA_defense: "Robust Aggregation for Federated Learning" https://arxiv.org/pdf/1912.13445
11. FoolsGoldDefense: "The Limitations of Federated Learning in Sybil Settings" https://www.usenix.org/system/files/raid20-fung.pdf
12. CoordinateWiseMedianDefense: "Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates" http://proceedings.mlr.press/v80/yin18a/yin18a.pdf
13. WbcDefense: "Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective" https://arxiv.org/abs/2110.13864
14. CRFLDefense: "CRFL: Certifiably Robust Federated Learning against Backdoor Attacks" http://proceedings.mlr.press/v139/xie21a/xie21a.pdf