View source for 18470961. RECURRING COMMUNICATION SCHEMES FOR FEDERATED LEARNING simplified abstract (QUALCOMM Incorporated)

=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 18470961 titled 'RECURRING COMMUNICATION SCHEMES FOR FEDERATED LEARNING

==Simplified Explanation==

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.

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

Potential Applications:
- Wireless communication systems
- Machine learning in wireless networks

Problems Solved:
- Facilitating federated learning in wireless communication
- Improving communication efficiency between UE and base station

Benefits:
- Enhanced machine learning capabilities in wireless networks
- Optimized communication protocols for federated learning

Potential Commercial Applications:
- Telecommunications companies
- IoT device manufacturers

Possible Prior Art:
- Previous systems may have used traditional machine learning methods without federated learning capabilities.

Unanswered Questions:
1. How does the federated learning configuration impact the overall performance of the wireless communication system?
2. Are there any specific security measures in place to protect the federated learning process in this context?


==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.



[[Category:H04W72/23]]
[[Category:G06N20/00]]