Qualcomm incorporated (20240135192). ZONE GRADIENT DIFFUSION (ZGD) FOR ZONE-BASED FEDERATED LEARNING simplified abstract
Contents
- 1 ZONE GRADIENT DIFFUSION (ZGD) FOR ZONE-BASED FEDERATED LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ZONE GRADIENT DIFFUSION (ZGD) FOR ZONE-BASED FEDERATED LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ZONE GRADIENT DIFFUSION (ZGD) FOR ZONE-BASED FEDERATED LEARNING
Organization Name
Inventor(s)
Vijaya Datta Mayyuri of San Diego CA (US)
ZONE GRADIENT DIFFUSION (ZGD) FOR ZONE-BASED FEDERATED LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135192 titled 'ZONE GRADIENT DIFFUSION (ZGD) FOR ZONE-BASED FEDERATED LEARNING
Simplified Explanation
The abstract describes a processor-implemented method for updating a central machine learning model in a federated learning system based on local and neighbor machine learning updates.
- The method involves receiving machine learning model updates from clients in a federated learning system.
- It determines fixed local zones for clients, each with a first fixed boundary.
- Model weights of the central machine learning model are updated based on local machine learning updates for a local subset of clients corresponding to the fixed local zone.
- Model weights are also updated based on neighbor machine learning updates for a neighbor subset of clients corresponding to a fixed neighbor zone neighboring the fixed local zone with a second fixed boundary.
- Neighbor machine learning updates have a different weight than local machine learning updates, determined by a similarity parameter.
Potential Applications
This technology can be applied in various fields such as healthcare, finance, and telecommunications for collaborative machine learning tasks where data privacy and security are crucial.
Problems Solved
1. Enhanced privacy and security in federated learning systems. 2. Efficient model updates based on local and neighbor machine learning updates.
Benefits
1. Improved model accuracy through collaborative learning. 2. Reduced communication costs in federated learning environments. 3. Enhanced data privacy and security for clients.
Potential Commercial Applications
Optimizing personalized recommendations in e-commerce platforms using federated learning.
Possible Prior Art
One possible prior art could be the use of differential privacy techniques in federated learning to enhance data security and privacy.
What are the potential scalability challenges of implementing this technology in large-scale federated learning systems?
Scalability challenges may arise in terms of managing a large number of clients, ensuring efficient communication, and handling diverse data distributions across different client subsets.
How can the similarity parameter be optimized to improve the performance of the central machine learning model in federated learning systems?
The similarity parameter can be optimized through empirical studies and experimentation to find the ideal weight for neighbor machine learning updates relative to local updates, balancing model accuracy and convergence speed.
Original Abstract Submitted
a processor-implemented method includes receiving machine learning model updates from clients in a federated learning system. the method also includes determining a fixed local zone associated with each of the clients, the fixed local zone having a first fixed boundary. the method includes updating model weights of a central machine learning model based on local machine learning updates for a local subset of the clients corresponding to the fixed local zone. the method includes updating the model weights of the central machine learning model based on neighbor machine learning updates for a neighbor subset of the clients. the neighbor subset corresponds to a fixed neighbor zone that neighbors the fixed local zone and has a second fixed boundary. the neighbor machine learning updates have a different weight than the local machine learning updates when updating model weights. a value of the different weight corresponds to a similarity parameter.
