本仓库收集了基于差分隐私的联邦学习的相关论文、以及相应代码。
提示：本仓库的代码均为本人个人实现，如有不准确的地方请联系本人，欢迎互相讨论。 本仓库的FL代码是基于 这个仓库 实现的，希望大家都能点赞多多支持，欢迎大家提交PR完善，谢谢！
注意，为了保证每个客户端被选中的次数是固定的（为了计算机每一次消耗的隐私预算），本代码使用了Round-robin的选择客户端机制，也就是说每个client是都是被顺序选择的。
(重要提示：本代码使用的FL本地更新轮数均为1，请勿更改，一旦更改本地迭代轮数，DP中的敏感度需要重新计算，敏感度上界会是一个很大的值，每一轮消耗的隐私预算会变得很多，请使用local epoch = 1的参数设置。)
数据集: MNIST, Cifar-10, FEMNIST, Fashion-MNIST, Shakespeare.
模型: CNN, MLP, LSTM for Shakespeare
DP机制: Laplace, Gaussian(Simple Composition), Gaussian(moments accountant)
DP参数:
DP裁剪: 在机器学习任务中，为了计算敏感度，通常情况下需要对梯度进行裁剪，限制梯度的一范数或者二范数的值。
实验环境：MNIST、高斯机制（Simple合成定理、Moment Account机制）、拉普拉斯机制。
实验命令:
pip3 install -r requirements.txt
bash run.sh绘图命令:
python3 draw.py
python main.py --dataset mnist --iid --model cnn --epochs 50 --dp_mechanism no_dp基于DP中的Simple Composition。
也就是说，如果某个客户端的隐私预算是
python main.py --dataset mnist --model cnn --epochs 50 --dp_mechanism Gaussian --dp_epsilon 10 --dp_delta 1e-5 --dp_clip 10本仓库使用Tensorflow Privacy来计算基于Gaussian Mechanism的Moment Account(MA)的噪声标准差。
python main.py --dataset mnist --model cnn --dp_mechanism MA --dp_epsilon 10 --dp_delta 1e-5 --dp_clip 10 --dp_sample 0.01以下论文可以提供更多信息。
Abadi, Martin, et al. "Deep learning with differential privacy." Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016.
基于DP中的Simple Composition。
python main.py --dataset mnist --model cnn --epochs 50 --dp_mechanism Laplace --dp_epsilon 30 --dp_clip 50- 综述
- Rodríguez-Barroso, Nuria, et al. "Federated Learning and Differential Privacy: Software tools analysis, the Sherpa. ai FL framework and methodological guidelines for preserving data privacy." Information Fusion 64 (2020): 270-292.
- 高斯机制
- Wei, Kang, et al. "Federated learning with differential privacy: Algorithms and performance analysis." IEEE Transactions on Information Forensics and Security 15 (2020): 3454-3469.
- Y. Zhou, et al.,"Optimizing the Numbers of Queries and Replies in Convex Federated Learning with Differential Privacy" in IEEE Transactions on Dependable and Secure Computing, 2023.
- K. Wei, et al.,"User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization" in IEEE Transactions on Mobile Computing, vol. 21, no. 09, pp. 3388-3401, 2022.
- Geyer, Robin C., Tassilo Klein, and Moin Nabi. "Differentially private federated learning: A client level perspective." arXiv preprint arXiv:1712.07557 (2017).
- Seif, Mohamed, Ravi Tandon, and Ming Li. "Wireless federated learning with local differential privacy." 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020.
- Mohammadi, Nima, et al. "Differential privacy meets federated learning under communication constraints." IEEE Internet of Things Journal (2021).
- Truex, Stacey, et al. "A hybrid approach to privacy-preserving federated learning." Proceedings of the 12th ACM workshop on artificial intelligence and security. 2019.
- Naseri, Mohammad, Jamie Hayes, and Emiliano De Cristofaro. "Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy." arXiv e-prints (2020): arXiv-2009.
- Malekzadeh, Mohammad, et al. "Dopamine: Differentially private federated learning on medical data." arXiv preprint arXiv:2101.11693 (2021).
- 拉普拉斯机制
- Wu, Nan, et al. "The value of collaboration in convex machine learning with differential privacy." 2020 IEEE Symposium on Security and Privacy (SP). IEEE, 2020.
- Y. Zhou, et al.,"Optimizing the Numbers of Queries and Replies in Convex Federated Learning with Differential Privacy" in IEEE Transactions on Dependable and Secure Computing, 2023.
- L. Cui, J. Ma, Y. Zhou and S. Yu, "Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses," in IEEE Transactions on Industrial Informatics, 2023.
- Liu, Xiaoyuan, et al. "Adaptive privacy-preserving federated learning." Peer-to-Peer Networking and Applications 13.6 (2020): 2356-2366.
- Zhao, Yang, et al. "Local differential privacy-based federated learning for internet of things." IEEE Internet of Things Journal 8.11 (2020): 8836-8853.
- Fu, Yao, et al. "On the practicality of differential privacy in federated learning by tuning iteration times." arXiv preprint arXiv:2101.04163 (2021).
- 其他机制
- Zhao, Yang, et al. "Local differential privacy-based federated learning for internet of things." IEEE Internet of Things Journal 8.11 (2020): 8836-8853.
- Truex, Stacey, et al. "LDP-Fed: Federated learning with local differential privacy." Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking. 2020.
- Yang, Jungang, et al. "Matrix Gaussian Mechanisms for Differentially-Private Learning." IEEE Transactions on Mobile Computing (2021).
- Sun, Lichao, Jianwei Qian, and Xun Chen. "Ldp-fl: Practical private aggregation in federated learning with local differential privacy." arXiv preprint arXiv:2007.15789 (2020).
- Liu, Ruixuan, et al. "Fedsel: Federated sgd under local differential privacy with top-k dimension selection." International Conference on Database Systems for Advanced Applications. Springer, Cham, 2020.
新版本用了Opacus进行 Per Sample Gradient Clip，限制了每一条Sample计算出的梯度的范数。
本代码设置了本地训练轮数为1，并且batch size为客户端本地的数据集大小，由于Opacus库的训练会保存所有样本的梯度，因此训练时gpu显存占用非常大。 解决此问题可以通过指定 --serial 和 --serial_bs 参数。
这两个参数会从物理上指定一个虚拟的batch size，相应的训练时间会变长，但是逻辑上不会影响训练和DP噪声的添加，这么做的主要原因是为了不违背DP噪声添加的理论。
Dev分支目前还在完善中，里面实现了包括MA、F-DP、以及Shuffle等新的DPFL算法，欢迎感兴趣的朋友提出宝贵意见！
考虑引用以下文章：
@ARTICLE{9504601,
author={Yang, Wenzhuo and Zhou, Yipeng and Hu, Miao and Wu, Di and Zheng, Xi and Wang, Jessie Hui and Guo, Song and Li, Chao},
journal={IEEE Internet of Things Journal},
title={Gain Without Pain: Offsetting DP-Injected Noises Stealthily in Cross-Device Federated Learning},
year={2022},
volume={9},
number={22},
pages={22147-22157},
keywords={Privacy;Internet of Things;Training;Distortion;Computational modeling;Machine learning;Differential privacy;Differential privacy (DP);federated learning (FL);secretly offsetting},
doi={10.1109/JIOT.2021.3102030}}@ARTICLE{9971780,
author={Hu, Miao and Yang, Wenzhuo and Luo, Zhenxiao and Liu, Xuezheng and Zhou, Yipeng and Chen, Xu and Wu, Di},
journal={IEEE Transactions on Mobile Computing},
title={AutoFL: A Bayesian Game Approach for Autonomous Client Participation in Federated Edge Learning},
year={2024},
volume={23},
number={1},
pages={194-208},
keywords={Training;Games;Costs;Computational modeling;Servers;Bayes methods;Task analysis;Bayesian game;client participation;federated edge learning;incomplete information},
doi={10.1109/TMC.2022.3227014}}[3] Y. Zhou et al., "Optimizing the Numbers of Queries and Replies in Convex Federated Learning with Differential Privacy," in IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/TDSC.2023.3234599.
[4] Y. Zhou, et al.,"Exploring the Practicality of Differentially Private Federated Learning: A Local Iteration Tuning Approach" in IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/TDSC.2023.3325889.
[5] Y. Yang, M. Hu, Y. Zhou, X. Liu and D. Wu, "CSRA: Robust Incentive Mechanism Design for Differentially Private Federated Learning," in IEEE Transactions on Information Forensics and Security, doi: 10.1109/TIFS.2023.3329441.