Device Heterogeneity in Federated Learning: A Superquantile Approach

The proliferation of mobile phones, wearables and edge devices has led to an unprecedented growth in the generation of user interaction data. Systems which tap into the power of this rich data while respecting the privacy of users are geared to lead the next generation of intelligent applications and devices. The leading distributed learning framework in this setting is federated learning [30]. In federated learning, a number of client devices with privacy-sensitive data collaboratively learn a machine learning model under the orchestration of a central server, while keeping their data decentralized. This is achieved by pushing the actual computation to the devices while the server coordinates with the devices for aggregation of model updates.