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