search

Yangfan-Jiang / Federated-Learning-with-Differential-Privacy

Implementation of dp-based federated learning framework using PyTorch
MIT License
273 stars 52 forks source link

Is there some paper about CDP-FedAVG-LS ？ Can you recommend the paper to me? #5

Closed bigdaronlee163 closed 2 years ago

Yangfan-Jiang commented 2 years ago

Hi, the code implementation is based on FedAvg algorithm [1] and moments accountant [2].

This repository is not a reproduction of any paper.

[1] McMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proc. Artificial Intelligence and Statistics (AISTATS), 2017.

[2] Abadi, Martin, et al. Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016.

bigdaronlee163 commented 2 years ago

I have a question. Why CDP instead of LDP? Isn't this where noise is added before sending weights to the server?

Yangfan-Jiang commented 2 years ago

Hi, I'm not sure what you mean by LDP/CDP in the FL setting. In this repository, we mainly focus on record-level DP, which, by definition, aims to hide the information of whether a data record (membership) participants in the model training.

Here we consider record-level DP for all FL parties and add DP noise before sending the model to the server to prevent a malicious server from conducting membership inference attack to FL parties.

A much stronger privacy guarantee can be provided by client-level DP [1], [2], which can hide the information of whether a certain party participant in the FL training.

[1] Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017) [2] H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018. Learning Differentially Private Recurrent Language Models. In International Conference on Learning Representations

bigdaronlee163 commented 2 years ago

I recently got the mathematical definition of differential privacy, but why do we need max_norm * sigma_g and max_norm when making Gaussian noise?

Yangfan-Jiang commented 2 years ago

Please refer to the following papers https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf https://arxiv.org/pdf/1607.00133.pdf