Extend vertical partitioned demonstration #25
Labels
Type: Epic 🤙
Describes a large amount of functionality that will likely be broken down into smaller issues
Milestone
Description
Extend the MVP (partitioning MNIST into images and labels) to work on arbitrary vertically partitioned datasets
Why?
The dataset/dataloader/data partitioning/splitNN architecture/PSI functions are coded assuming the provided data is MNIST and the partitioning function split images and labels. Fortunately, the real world has more data than just MNIST. In this epic we will generalise the code to work with many datasets and partitions
Breakdown
IN PROGRESS
Who else?
May require work in PySyft and PSI
Additional Context
Should be completed after #2 and #3The text was updated successfully, but these errors were encountered: