What is federated learning? How does it differ from traditional large-scale machine learning, distributed optimization, and privacy-preserving data analysis? Learn from the blog post by Carnegie Mellon University.
Mobile phones, wearable devices, and autonomous vehicles are just a few of the modern distributed networks generating a wealth of data each day. Due to the growing computational power of these devices—coupled with concerns about transmitting private information—it is increasingly attractive to store data locally and push network computation to the edge devices.
The article deals with:
- What is federated learning?
- What are the challenges in federated learning?
- Federated Learning @ CMU
- Future Directions
Much more detail in the article. These challenging problems (and more) will require collaborative efforts from a wide range of research communities. Good read!
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