Dual-Headed SplitNN Config1 and Config2 #68
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TTitcombe
reviewed
Aug 26, 2020
| "model_locations = [client_1, client_2, server, label_owner]\n", | ||
| "\n", | ||
| "#Split each image and send one part to client_1, and other to client_2\n", | ||
| "distributed_trainloader = Distribute_MNIST(data_owners=data_owners, data_loader=trainloader)" |
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For the case where images go to one worker and labels to another, we have implemented the vertically_federate method of a PartitionedDataset (https://github.com/OpenMined/PyVertical/blob/master/src/future/dataset.py#L219).
Could you change this to use that class?
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Actually, the image is split into two parts, that are sent to client_1 and client_2. and label_owner has labels.
PyVertical/src/future/dataset.py
Line 194 in 5f3f9c1
I don't think this supports the case.
Would you like me to write a new method for splitting the image in
PartitionedDataset among two workers?
TTitcombe
reviewed
Aug 26, 2020
| "model_locations = [client_1, client_2, server]\n", | ||
| "\n", | ||
| "#Split each image and send one part to client_1, and other to client_2\n", | ||
| "distributed_trainloader = Distribute_MNIST(data_owners=data_owners, data_loader=trainloader)" |
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Same comment as on the other notebook
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Description
Added two configurations of splitNN for vertically partitioned data.
Config-1: Server has image labels.
Config-2: A worker named label_owner has image labels (U-shaped).
Affected Dependencies
None
Checklist