View source for 20240054352. COMPUTER IMPLEMENTED MULTI-LAYER FEDERATED LEARNING METHOD BASED ON DISTRIBUTED CLUSTERING AND NON-TRANSITORY COMPUTER READABLE MEDIUM THEREOF simplified abstract (ASIA UNIVERSITY)

=COMPUTER IMPLEMENTED MULTI-LAYER FEDERATED LEARNING METHOD BASED ON DISTRIBUTED CLUSTERING AND NON-TRANSITORY COMPUTER READABLE MEDIUM THEREOF=


==Organization Name==

[[:Category:ASIA UNIVERSITY|ASIA UNIVERSITY]]


[[Category:ASIA UNIVERSITY]]

==Inventor(s)==

[[:Category:ZON-YIN Shae of TAIPEI CITY (TW)|ZON-YIN Shae of TAIPEI CITY (TW)]][[Category:ZON-YIN Shae of TAIPEI CITY (TW)]]

[[:Category:KUN-YI Chen of TAICHUNG CITY (TW)|KUN-YI Chen of TAICHUNG CITY (TW)]][[Category:KUN-YI Chen of TAICHUNG CITY (TW)]]

[[:Category:JING-PHA Tsai of TAICHUNG CITY (TW)|JING-PHA Tsai of TAICHUNG CITY (TW)]][[Category:JING-PHA Tsai of TAICHUNG CITY (TW)]]

[[:Category:CHI-YU Chang of TAICHUNG CITY (TW)|CHI-YU Chang of TAICHUNG CITY (TW)]][[Category:CHI-YU Chang of TAICHUNG CITY (TW)]]

[[:Category:YUAN-YU Tsai of TAICHUNG CITY (TW)|YUAN-YU Tsai of TAICHUNG CITY (TW)]][[Category:YUAN-YU Tsai of TAICHUNG CITY (TW)]]

==COMPUTER IMPLEMENTED MULTI-LAYER FEDERATED LEARNING METHOD BASED ON DISTRIBUTED CLUSTERING AND NON-TRANSITORY COMPUTER READABLE MEDIUM THEREOF - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240054352 titled 'COMPUTER IMPLEMENTED MULTI-LAYER FEDERATED LEARNING METHOD BASED ON DISTRIBUTED CLUSTERING AND NON-TRANSITORY COMPUTER READABLE MEDIUM THEREOF

==Simplified Explanation==

The abstract describes a multi-layer federated learning method based on distributed clustering. 

* Computing feature distribution similarity for participating nodes with non-iid data sets and grouping nodes into clusters based on this similarity.
* Updating local models of nodes in each cluster using a federated learning algorithm.
* Inputting nodes into a multi-layer aggregation mechanism.
* Terminating the aggregation mechanism when clustering result meets required demand.
* Implementing a blockchain-based multi-layer federated learning system with various modules.

**Potential Applications:**
- This technology can be applied in industries where data privacy is a concern, such as healthcare and finance.
- It can also be used in IoT devices for collaborative learning without sharing raw data.

**Problems Solved:**
- Addresses the issue of data privacy by allowing nodes to learn collaboratively without sharing sensitive information.
- Improves learning performance by utilizing distributed clustering to group nodes with similar data distributions.

**Benefits:**
- Enhanced data privacy protection.
- Improved learning performance through distributed clustering.
- Scalability and efficiency in federated learning processes.


==Original Abstract Submitted==

a multi-layer federated learning method based on distributed clustering is provided, which comprises the following steps. computing a feature distribution similarity for each of participating nodes with non-(non-independent and identically distributed) data sets, and grouping these nodes into plural clusters by the feature distribution similarity. updating local model of nodes of each cluster by a federated learning algorithm, and inputting these nodes into a multi-layer aggregation mechanism. terminating the operation of the multi-layer aggregation mechanism until the clustering result meets a required demand. furthermore, we implement a blockchain-based multi-layer federated learning system, including model aggregation module, api module, time-series synchronization module, and ipfs, based on distributed clustering architecture. the learning performance is proven to effectively improved.



[[Category:G06N3/098]]