Combining split and federated architectures for efficiency and privacy in deep learning

Distributed learning systems are increasingly being adopted for a variety of applications as centralized training becomes unfeasible. A few architectures have emerged to divide and conquer the computational load, or to run privacy-aware deep learning models, using split or federated learning. Each architecture has benefits and drawbacks. In this work, we compare the efficiency and privacy performance of two distributed learning architectures that combine the principles of split and federated learning, trying to get the best of both. In particular, our design goal is to reduce the computational power required by each client in Federated Learning and to parallelize Split Learning. We share some initial lessons learned from our implementation that leverages the PySyft and PyGrid libraries.