Collaborative learning aims to improve the performance of homongeneous/heterogeneous models via collaborative intelligence. It enables a lot of use cases, such as:
- Federated learning: privacy-preserving collaborative training on distributed devices.
- Ensemble learning: combining the predictions from multiple models to improve the performance.
- Multi-task learning: learning multiple tasks simultaneously.
In this blog, we summarize recent works on collaborative learning.
Table of contents