18424645. Private Federated Learning with Reduced Communication Cost simplified abstract (Google LLC)
Contents
- 1 Private Federated Learning with Reduced Communication Cost
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Private Federated Learning with Reduced Communication Cost - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Private Federated Learning
- 1.13 Original Abstract Submitted
Private Federated Learning with Reduced Communication Cost
Organization Name
Inventor(s)
Peter Kairouz of Seattle WA (US)
Christopher Choquette-choo of Sunnyvale CA (US)
Md Enayat Ullah of Baltimore MD (US)
Private Federated Learning with Reduced Communication Cost - A simplified explanation of the abstract
This abstract first appeared for US patent application 18424645 titled 'Private Federated Learning with Reduced Communication Cost
Simplified Explanation
New techniques are introduced to reduce communication in private federated learning without the need for setting compression rates. These methods adjust the compression rate based on training error while ensuring privacy through secure aggregation and differential privacy.
Key Features and Innovation
- Techniques to reduce communication in private federated learning
- On-the-fly adjustment of compression rates based on training error
- Provable privacy guarantees through secure aggregation and differential privacy
Potential Applications
The technology can be applied in various fields such as healthcare, finance, and telecommunications where privacy and communication efficiency are crucial.
Problems Solved
The technology addresses the challenge of reducing communication overhead in private federated learning while maintaining privacy and data security.
Benefits
- Improved communication efficiency
- Enhanced privacy protection
- Simplified implementation without the need for manual tuning
Commercial Applications
This technology can be utilized in industries that require secure and efficient communication protocols, such as data analytics firms, healthcare providers, and financial institutions.
Prior Art
Readers can explore prior research on federated learning, secure aggregation, and differential privacy to understand the evolution of these technologies.
Frequently Updated Research
Researchers are continuously exploring new methods to enhance communication efficiency and privacy in federated learning systems. Stay updated on the latest advancements in this field.
Questions about Private Federated Learning
1. What are the key challenges in implementing secure communication in federated learning?
- Secure communication in federated learning involves ensuring data privacy, maintaining communication efficiency, and implementing robust security protocols.
2. How does differential privacy contribute to enhancing privacy guarantees in federated learning?
- Differential privacy adds noise to the data to protect individual privacy while still allowing for accurate aggregate analysis.
Original Abstract Submitted
New techniques are provided which reduce communication in private federated learning without the need for setting or tuning compression rates. Example on-the-fly methods automatically adjust the compression rate based on the error induced during training, while maintaining provable privacy guarantees through the use of secure aggregation and differential privacy.
