Bridging communities: TensorFlow Federated (TFF) and OpenMined

, and provides a framework for implementing algorithms that - the current gold standard. The FL algorithms that enable us to achieve these guarantees can be used to process data in datacenter deployments, even in scenarios where - as is the case here with the PySyft database - all of that data resides in a single administrative domain.

To see this, just imagine that for each user in the database, we draw a virtual boundary around all their data, and think of it as a kind of virtual silo. We can treat such virtual silos of user data in the same way as how we treat “client” devices in a more traditional FL setting, and orchestrate FL algorithms to run across virtual silos as clients.

Thus, for example, when training an ML model, we’d repeatedly pick sets of users from the database, locally and independently train local model updates on their data - separately for each user, add clipping to each local update and noise for privacy, aggregate these local updates across users to produce an updated global model, and repeat this process for thousands of rounds until the ML model converges, as shown below.