View source for Qualcomm incorporated (20240137944). RECURRING COMMUNICATION SCHEMES FOR FEDERATED LEARNING simplified abstract

=RECURRING COMMUNICATION SCHEMES FOR FEDERATED LEARNING=


==Organization Name==

[[:Category:qualcomm incorporated|qualcomm incorporated]]


[[Category:qualcomm incorporated]]

==Inventor(s)==

[[:Category:Hamed Pezeshki of San Diego CA (US)|Hamed Pezeshki of San Diego CA (US)]][[Category:Hamed Pezeshki of San Diego CA (US)]]

[[:Category:Tao Luo of San Diego CA (US)|Tao Luo of San Diego CA (US)]][[Category:Tao Luo of San Diego CA (US)]]

[[:Category:Sony Akkarakaran of Poway CA (US)|Sony Akkarakaran of Poway CA (US)]][[Category:Sony Akkarakaran of Poway CA (US)]]

==RECURRING COMMUNICATION SCHEMES FOR FEDERATED LEARNING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240137944 titled 'RECURRING COMMUNICATION SCHEMES FOR FEDERATED LEARNING

==Simplified Explanation==

The abstract of the patent application describes a system where a user equipment (UE) receives a federated learning configuration from a base station to facilitate machine learning associated with a periodic communication scheme.

* User equipment (UE) receives a federated learning configuration from a base station.
* The configuration indicates a recurring communication scheme for machine learning.
* UE communicates with the base station based on the federated learning configuration.

== Potential Applications ==
This technology could be applied in various industries such as telecommunications, IoT, and data analytics for improving machine learning models through federated learning.

== Problems Solved ==
1. Enhances machine learning capabilities in wireless communication systems.
2. Facilitates efficient communication between user equipment and base stations for federated learning.

== Benefits ==
1. Improved machine learning performance.
2. Enhanced communication efficiency.
3. Better utilization of wireless resources.

== Potential Commercial Applications ==
Optimizing machine learning algorithms in telecommunications networks, enhancing data analytics in IoT devices, and improving predictive maintenance systems in various industries.

== Possible Prior Art ==
There are existing patents related to federated learning in wireless communication systems, but specific prior art related to the described technology would need to be researched further.

=== Unanswered Questions ===

=== How does this technology impact network latency in wireless communication systems? ===
This article does not delve into the potential effects of this technology on network latency in wireless communication systems. 

=== What are the potential security implications of implementing federated learning in wireless communication systems? ===
The article does not address the security concerns that may arise from implementing federated learning in wireless communication systems.


==Original Abstract Submitted==

various aspects of the present disclosure generally relate to wireless communication. in some aspects, a user equipment (ue) may receive, from a base station, a federated learning configuration indicating a recurring communication scheme such as a periodic communication scheme for communicating with the base station to facilitate federated learning associated with a machine learning component. the ue may communicate with the base station based at least in part on the federated learning configuration. numerous other aspects are provided.



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[[Category:G06N20/00]]