Federated learning (FL) is a new machine learning setting introduced in 2016 in a sequence of papers resulting from a collaboration between a Google team led by Brendan McMahan and Peter Richtarik’s group.
Key idea: Many clients (e.g., mobile phones, IoT devices or organizations) collaboratively train a machine learning model under the orchestration of a central trusted server, while keeping the training data stored on the client devices in a decentralized fashion in order to protect privacy.
Split learning (SL) is a new federated learning tool specifically developed for training of deep neural networks.
Key idea: Cut the network model to be trained at a certain layer, splitting into two parts: one stored at the clients and one stored on a server.
Project Goals:
Enhance split learning with communication compression
Develop attacks on split learning systems and measures to mitigate such attacks
Develop a practical split learning system for edge devices
Investigator:
