Meta platforms technologies, llc (20240346376). GROUP PERSONALIZED FEDERATED LEARNING simplified abstract
Contents
GROUP PERSONALIZED FEDERATED LEARNING
Organization Name
meta platforms technologies, llc
Inventor(s)
Fuchun Peng of Palo Alto CA (US)
GROUP PERSONALIZED FEDERATED LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346376 titled 'GROUP PERSONALIZED FEDERATED LEARNING
Simplified Explanation: The patent application describes a system and method for enabling group personalized federated learning. This involves providing a global machine learning model to communication devices, receiving model parameters from these devices based on local training data, grouping the devices, assigning group-specific machine learning models, and providing these models to subsets of devices within the groups.
- Key Features and Innovation:
- Global machine learning model generated through federated learning - Communication devices provide model parameters based on local training data - Grouping of devices and assignment of group-specific machine learning models - Subsets of devices receive respective group-specific models
- Potential Applications:
- Collaborative learning in IoT devices - Personalized recommendations in mobile applications - Enhanced privacy in machine learning models
- Problems Solved:
- Addressing data privacy concerns in centralized machine learning - Improving model accuracy through personalized learning - Reducing communication bandwidth for model updates
- Benefits:
- Enhanced privacy protection - Improved model accuracy - Efficient model updates for large device networks
- Commercial Applications:
- "Group Personalized Federated Learning System for IoT Devices" - Potential use in healthcare for personalized treatment recommendations - Market implications in smart home devices for customized user experiences
- Prior Art:
- Prior research on federated learning in mobile devices - Studies on personalized machine learning models in collaborative environments
- Frequently Updated Research:
- Ongoing studies on federated learning optimization techniques - Research on privacy-preserving machine learning algorithms
Questions about Group Personalized Federated Learning: 1. How does group personalized federated learning differ from traditional federated learning methods? 2. What are the potential challenges in implementing group personalized federated learning in large-scale device networks?
1. A relevant generic question not answered by the article, with a detailed answer: How does group personalized federated learning contribute to the advancement of machine learning models in decentralized environments? Group personalized federated learning enhances model accuracy and privacy protection by tailoring machine learning models to specific groups of devices, leading to more efficient and effective learning processes.
2. Another relevant generic question, with a detailed answer: What are the implications of group personalized federated learning for data privacy and security? Group personalized federated learning addresses privacy concerns by keeping data localized to individual devices and groups, reducing the risk of data breaches and unauthorized access to sensitive information.
Original Abstract Submitted
a system and method facilitating group personalized federated learning are provided. the system may provide a global machine learning model, generated based on federated learning, to communication devices. the system may also receive model parameters from the communication devices based in part on the communication devices determining local training data generated by the communication devices implementing the global machine learning model. the system may also determine, based on the model parameters from the communication devices, groups of the communication devices and may assign a group specific machine learning model(s), among a plurality of group specific machine learning models, to the groups. the plurality of group specific machine learning models may be associated with the global machine learning model. the system may also provide respective group specific machine learning models, among the plurality of group specific machine learning models, to subsets of communication devices of the groups of the communication devices.
