View source for 17540660. PRIVACY-PRESERVING CLASS LABEL STANDARDIZATION IN FEDERATED LEARNING SETTINGS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

=PRIVACY-PRESERVING CLASS LABEL STANDARDIZATION IN FEDERATED LEARNING SETTINGS=


==Organization Name==

[[:Category:INTERNATIONAL BUSINESS MACHINES CORPORATION|INTERNATIONAL BUSINESS MACHINES CORPORATION]]


[[Category:INTERNATIONAL BUSINESS MACHINES CORPORATION]]

==Inventor(s)==

[[:Category:Shonda Adena Witherspoon of Yorktown Heights NY (US)|Shonda Adena Witherspoon of Yorktown Heights NY (US)]][[Category:Shonda Adena Witherspoon of Yorktown Heights NY (US)]]

[[:Category:Ramasuri Narayanam of Bangalore (IN)|Ramasuri Narayanam of Bangalore (IN)]][[Category:Ramasuri Narayanam of Bangalore (IN)]]

[[:Category:Hima Patel of Bengaluru (IN)|Hima Patel of Bengaluru (IN)]][[Category:Hima Patel of Bengaluru (IN)]]

[[:Category:Sameep Mehta of Bangalore (IN)|Sameep Mehta of Bangalore (IN)]][[Category:Sameep Mehta of Bangalore (IN)]]

==PRIVACY-PRESERVING CLASS LABEL STANDARDIZATION IN FEDERATED LEARNING SETTINGS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17540660 titled 'PRIVACY-PRESERVING CLASS LABEL STANDARDIZATION IN FEDERATED LEARNING SETTINGS

==Simplified Explanation==

The patent application describes methods, systems, and computer program products for privacy-preserving class label standardization in federated learning settings. 

* The method involves determining a signature for each class of data for multiple client devices in a federated learning environment using data privacy-preserving techniques.
* Signature matches are identified across the client devices, and class labels are generated for the matched data.
* The matched data is then labeled with the generated class labels across the client devices.
* Automated actions can be performed based on the labeled data.

Potential applications of this technology:

* Federated learning: The technology can be used in federated learning settings where multiple client devices collaborate to train a machine learning model without sharing their raw data.
* Privacy-preserving data analysis: The methods and systems described in the patent application enable privacy-preserving analysis of data across multiple client devices.
* Standardization of class labels: The technology provides a way to standardize class labels across different client devices, ensuring consistency in the analysis and interpretation of data.

Problems solved by this technology:

* Privacy concerns in federated learning: The methods and systems address privacy concerns by using data privacy-preserving techniques to determine signatures and match data without exposing sensitive information.
* Inconsistent class labels: The technology solves the problem of inconsistent class labels across different client devices by generating standardized labels based on signature matches.

Benefits of this technology:

* Privacy preservation: The methods and systems ensure that sensitive data remains private during the analysis process.
* Improved data analysis: By standardizing class labels, the technology improves the accuracy and reliability of data analysis across multiple client devices.
* Efficient collaboration: The technology enables efficient collaboration in federated learning settings by allowing client devices to share and analyze data without compromising privacy.


==Original Abstract Submitted==

Methods, systems, and computer program products for privacy-preserving class label standardization in federated learning settings are provided herein. A computer-implemented method includes determining, using one or more data privacy-preserving techniques, a signature for each of one or more classes of data for each of multiple client devices within a federated learning environment; identifying one or more signature matches across at least a portion of the multiple client devices; generating one or more class labels for the one or more classes of data associated with the one or more signature matches; labeling, across the at least a portion of the multiple client devices, the one or more classes of data associated with the one or more signature matches with the one or more generated class labels; and performing one or more automated actions based at least in part on the one or more labeled classes of data.



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[[Category:G06N20/00]]