Lieberman provides a step-by-step guide to understanding and developing with federated data networks. This includes understanding the basics of decentralization and federated learning, exploring data security and privacy, playing with frameworks such as PySyft, Flower, TensorFlow Federated, finding applications that require the technology, and getting involved in the community around decentralized data sharing and federated learning.
Key takeaways:
- Confidential computing and federated data networks (FDNs) are secure technologies that can revolutionize data sharing in the digital age, enhancing data security and facilitating access to distributed and siloed data.
- Federated data networks are like a team of libraries, where each library retains control of its books (data), and confidential computing ensures that the data remains encrypted and protected even when it is being processed.
- These technologies can be particularly useful in industries such as healthcare and finance, where multiple parties need to collaborate on data but also maintain privacy and comply with strict regulations.
- Getting started with federated data networks involves understanding the principles of decentralization and federated learning, exploring data security and privacy, playing with relevant frameworks, finding applicable use cases, and getting involved in the community.