Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
This work addresses the problem of scalable on-device personalization for ML systems, but it is incremental as it builds on existing federated learning concepts.
The paper tackles the design of a federated task processing system for on-device personalization, originally supporting evaluation and tuning, with recent additions for federated learning of deep neural networks, and includes comparisons to another system and large-scale use cases.
We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML systems, primarily for the purpose of personalizing these systems. In recent years, support for an additional federated task has been added: federated learning (FL) of deep neural networks. To our knowledge, only one other system has been described in literature that supports FL at scale. We include comparisons to that system to help discuss design decisions and attached trade-offs. Finally, we describe two specific large scale personalization use cases in detail to showcase the applicability of federated tuning to on-device personalization and to highlight application specific solutions.