Private federated learning on vertically partitioned data via entity resolutionand additively homomorphic encryption

Private federated learning that keeps data secret and still helps

Two companies hold different facts about the same people, but neither wants to hand over their raw files or say who they both have, so they learn together with federated learning.
They first match records privately, then run math on locked numbers so no one sees raw rows.
The system uses private matching and strong encryption, numbers stays hidden while updates travel, only small messages cross the network.
Matching can make mistakes, yes, but a few errors usually won't ruin the outcome, the method tolerates them rather well.
In trials it learns nearly as good like when all data put in one place, and it scales to millions of entries, many features, fast enough.
That means partners can combine info and keep control, which boosts results for everyone while preserving privacy.
Even if someone tries to peek, they can't read the other side's details.
Better matching gives bigger gains, so careful entity matching pays off — shared models that help without sharing raw data.

Read article comprehensive review in Paperium.net:
Private federated learning on vertically partitioned data via entity resolutionand additively homomorphic encryption

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