Merge branch 'main' into update-doc

README.md

@@ -29,7 +29,7 @@
 
 # What is AIJack?
 
-AIJack is an easy-to-use open-source simulation tool for testing the security of your AI system against hijackers. It provides advanced security techniques like *Differential Privacy*, *Homomorphic Encryption*, *K-anonymity* and *Federated Learning* to guarantee protection for your AI. With AIJack, you can test and simulate defenses against various attacks such as *Poisoning*, *Model Inversion*, *Backdoor*, and *Free-Rider*. We support more than 30 state-of-the-art methods. For more information, check our [documentation](https://koukyosyumei.github.io/AIJack/) and start securing your AI today with AIJack.
+AIJack is an easy-to-use open-source simulation tool for testing the security of your AI system against hijackers. It provides advanced security techniques like *Differential Privacy*, *Homomorphic Encryption*, *K-anonymity* and *Federated Learning* to guarantee protection for your AI. With AIJack, you can test and simulate defenses against various attacks such as *Poisoning*, *Model Inversion*, *Backdoor*, and *Free-Rider*. We support more than 30 state-of-the-art methods. For more information, check our [paper](https://arxiv.org/abs/2312.17667) and [documentation](https://koukyosyumei.github.io/AIJack/) and start securing your AI today with AIJack.
 
 # Installation
 
@@ -237,6 +237,10 @@ Below you can find a list of papers and books that either use or extend AIJack.
 - Huang, Shiyuan. A General Framework for Model Adaptation to Meet Practical Constraints in Computer Vision. Diss. Columbia University, 2024.
 - Liu, Can, Jin Wang, and Dongyang Yu. "RAF-GI: Towards Robust, Accurate and Fast-Convergent Gradient Inversion Attack in Federated Learning." arXiv preprint arXiv:2403.08383 (2024).
 
+# Contribution
+
+AIJack welcomes contributions of any kind. If you'd like to address a bug or propose a new feature, please refer to [our guide](docs/source/contribution.rst).
+
 # Contact
 
 welcome2aijack[@]gmail.com

src/aijack/defense/dp/manager/accountant.py

@@ -73,6 +73,7 @@ def calc_upperbound_of_rdp_onestep(self, alpha, noise_params, sampling_rate):
         Returns:
             float: Upper bound of RDP for one step.
         """
+
         key = hash(
             f"{alpha}_{list(noise_params.keys())[0]}_{list(noise_params.values())[0]}_{sampling_rate}"
         )
@@ -101,6 +102,7 @@ def _calc_upperbound_of_rdp(self, lam, steps_info):
         Returns:
             float: Upper bound of RDP.
         """
+
         rdp = 0.0
         for noise_params, sampling_rate, num_steps in steps_info:
             rdp += num_steps * self.calc_upperbound_of_rdp_onestep(