  Data privacy and ownership are significant in social data science, raising
legal and ethical concerns. Sharing and analyzing data is difficult when
different parties own different parts of it. An approach to this challenge is
to apply de-identification or anonymization techniques to the data before
collecting it for analysis. However, this can reduce data utility and increase
the risk of re-identification. To address these limitations, we present PADME,
a distributed analytics tool that federates model implementation and training.
PADME uses a federated approach where the model is implemented and deployed by
all parties and visits each data location incrementally for training. This
enables the analysis of data across locations while still allowing the model to
be trained as if all data were in a single location. Training the model on data
in its original location preserves data ownership. Furthermore, the results are
not provided until the analysis is completed on all data locations to ensure
privacy and avoid bias in the results.
