Nokia technologies oy (20240338574). FEDERATED LEARNING simplified abstract
Contents
FEDERATED LEARNING
Organization Name
Inventor(s)
Soumyajit Chatterjee of Cambridge (GB)
Akhil Mathur of Cambridge (GB)
FEDERATED LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338574 titled 'FEDERATED LEARNING
Simplified Explanation: The patent application relates to a method, apparatus, and computer program for federated learning in computational models. The apparatus determines if a computational model can be trained locally on a client device within a target time. If not, it selects a modified version that can be trained within the target time.
- The apparatus determines if a computational model can be trained locally on a client device within a target time.
- If not, it selects a modified version of the model that can be trained within the target time.
- The selected modified model is provided for local training on the client device.
Potential Applications: 1. Enhancing privacy in machine learning by training models locally on client devices. 2. Improving efficiency in training computational models by adapting to device resources. 3. Facilitating collaborative learning across multiple devices without sharing raw data.
Problems Solved: 1. Addressing privacy concerns by enabling local training of models. 2. Optimizing training time by selecting suitable model architectures. 3. Promoting collaboration in machine learning while maintaining data privacy.
Benefits: 1. Enhanced privacy protection for sensitive data. 2. Improved efficiency in training computational models. 3. Facilitated collaboration in machine learning tasks.
Commercial Applications: Potential commercial applications include:
- Mobile applications that require personalized machine learning models.
- Edge computing devices for real-time data processing.
- Collaborative platforms for distributed machine learning tasks.
Questions about Federated Learning for Computational Models: 1. How does federated learning differ from traditional centralized machine learning approaches? 2. What are the key challenges in implementing federated learning for computational models?
Frequently Updated Research: Stay updated on advancements in federated learning algorithms and techniques for improving model training on client devices.
Original Abstract Submitted
example embodiments relate to an apparatus, method and computer program relating to federated learning for computational models. in an example, an apparatus comprises means for determining, based on one or more resources of a client device, whether a first computational model architecture can be trained locally by the client device within a target training time. the apparatus may also comprise means for selecting, if the first computational model architecture cannot be trained locally by the client device within the target training time, a modified version of the first computational model architecture that can be trained by the client device within the target training time. the apparatus may also comprise means for providing the selected modified version of the first computational model architecture for local training by the client device.
