thupchnsky/mufc
A federated clustering approach with the corresponding unlearning mechanism (ICLR 2023)
This project helps researchers and data scientists analyze large, sensitive datasets like patient records or financial data without centralizing them. It takes distributed datasets from multiple sources and produces a K-means clustering analysis, while also providing a way to remove specific data points from the analysis if needed for privacy or compliance. This allows for collaborative insights without compromising individual data privacy.
No commits in the last 6 months.
Use this if you need to perform K-means clustering on data that cannot be combined into a single location due to privacy, security, or logistical constraints.
Not ideal if your data is already centralized and privacy unlearning is not a primary concern for your clustering task.
Stars
22
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2
Language
Python
License
MIT
Category
Last pushed
Oct 12, 2023
Commits (30d)
0
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