Federated Learning
Mark Jackson
This comprehensive guide demystifies federated learning, a technique that allows machine learning models to be trained across multiple decentralized devices or servers while keeping the data local. By focusing on privacy and security, federated learning enables organizations to leverage the vast amounts of data available without compromising individual privacy.
Federated Learning: Privacy-Preserving Machine Learning in the Decentralized Age is an essential read for anyone interested in the intersection of privacy, machine learning, and decentralized systems. It provides a thorough understanding of how federated learning works and its potential to reshape the future of data privacy and AI.
Duration - 3h 11m.
Author - Mark Jackson.
Narrator - Rayan Mitchell.
Published Date - Wednesday, 22 January 2025.
Copyright - © 2025 Mark Jackson ©.
Location:
United States
Description:
This comprehensive guide demystifies federated learning, a technique that allows machine learning models to be trained across multiple decentralized devices or servers while keeping the data local. By focusing on privacy and security, federated learning enables organizations to leverage the vast amounts of data available without compromising individual privacy. Federated Learning: Privacy-Preserving Machine Learning in the Decentralized Age is an essential read for anyone interested in the intersection of privacy, machine learning, and decentralized systems. It provides a thorough understanding of how federated learning works and its potential to reshape the future of data privacy and AI. Duration - 3h 11m. Author - Mark Jackson. Narrator - Rayan Mitchell. Published Date - Wednesday, 22 January 2025. Copyright - © 2025 Mark Jackson ©.
Language:
English
Opening Credits
Duration:00:00:13
Introduction
Duration:00:11:58
Chapter 1 Foundations of federated learning
Duration:00:16:57
Chapter 2 Technical overview
Duration:00:27:22
Chapter 3 Privacy and security privacy challenges
Duration:00:19:16
Chapter 4 Applications and use cases
Duration:00:15:29
Chapter 5 Challenges and limitations
Duration:00:20:51
Chapter 6 Future directions
Duration:00:30:54
Chapter 7 Practical implementation
Duration:00:27:59
Conclusion
Duration:00:20:23
Ending Credits
Duration:00:00:13