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An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawitz et. al)" in Python.

Python 100.00%
federated-learning secure-aggregation python

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secureaggregation's Issues

Inquiry about the implementation of "Secure Single-Server Aggregation with (Poly)Logarithmic Overhead" paper

Dear Ammar Tahir:

I hope this email finds you well. I came across your implementation of "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawitz et. al)" on GitHub and was quite impressed with your work. I appreciate your contributions in this field.

I am currently conducting research in the area of secure aggregation for privacy-preserving machine learning, and I recently came across another paper by Bonawitz et al. titled "Secure Single-Server Aggregation with (Poly)Logarithmic Overhead." I was wondering if you have also implemented this follow-up paper or if you have any knowledge of existing implementations.

If you have any information or resources related to the implementation of "Secure Single-Server Aggregation with (Poly)Logarithmic Overhead," I would be grateful if you could share them with me. Your expertise and insights would greatly benefit my research.

Thank you for your time and consideration. I look forward to hearing from you.

Best regards

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