View source for 17544581. POWER CONTROL IN OVER THE AIR AGGREGATION FOR FEDERATED LEARNING simplified abstract (QUALCOMM Incorporated)

=POWER CONTROL IN OVER THE AIR AGGREGATION FOR FEDERATED LEARNING=


==Organization Name==

[[:Category:QUALCOMM Incorporated|QUALCOMM Incorporated]]


[[Category:QUALCOMM Incorporated]]

==Inventor(s)==

[[:Category:Eren Balevi of San Diego CA (US)|Eren Balevi of San Diego CA (US)]][[Category:Eren Balevi of San Diego CA (US)]]

[[:Category:Taesang Yoo of San Diego CA (US)|Taesang Yoo of San Diego CA (US)]][[Category:Taesang Yoo 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:Srinivas Yerramalli of San Diego CA (US)|Srinivas Yerramalli of San Diego CA (US)]][[Category:Srinivas Yerramalli of San Diego CA (US)]]

[[:Category:Junyi Li of Fairless Hills PA (US)|Junyi Li of Fairless Hills PA (US)]][[Category:Junyi Li of Fairless Hills PA (US)]]

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

==POWER CONTROL IN OVER THE AIR AGGREGATION FOR FEDERATED LEARNING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17544581 titled 'POWER CONTROL IN OVER THE AIR AGGREGATION FOR FEDERATED LEARNING

==Simplified Explanation==

The patent application describes a system for coordinating federated learning among multiple user equipment (UEs) using over-the-air (OTA) aggregation with power control to mitigate aggregation distortion due to amplitude misalignment.

* The system includes a parameter server located at a base station.
* The parameter server selects a group of UEs for an OTA aggregation session based on a common received power property of each UE in the group.
* The parameter server transmits a global model to the selected UEs.
* Each UE in the group trains the global model based on its local dataset and transmits values associated with the trained local model.
* The parameter server receives a combined response from the group of UEs in the form of a first aggregate amplitude modulated analog signal.

Potential applications of this technology:

* Federated learning in wireless communication systems.
* Coordinating machine learning tasks among multiple user equipment.
* Improving the efficiency and accuracy of federated learning in wireless networks.

Problems solved by this technology:

* Aggregation distortion due to amplitude misalignment in federated learning.
* Coordinating and managing federated learning tasks among multiple user equipment.
* Ensuring efficient and accurate training of global models in wireless communication systems.

Benefits of this technology:

* Mitigates aggregation distortion and improves the accuracy of federated learning.
* Enables efficient coordination and management of federated learning tasks.
* Reduces the complexity and resource requirements of federated learning in wireless networks.


==Original Abstract Submitted==

A parameter server located at a base station may coordinate federated learning among multiple user equipment (UEs) using over-the-air (OTA) aggregation with power control to mitigate aggregation distortion due to amplitude misalignment. The parameter server may select a first group of UEs for a first OTA aggregation session of a federated learning round based on a common received power property of each UE in the first group of UEs. The parameter server may transmit a global model to the first group of UEs. Each UE in the first group may train the global model based on a local dataset and transmit values associated with the trained local model. The parameter server may receive, on resource elements for the first group of UEs, a first aggregate amplitude modulated analog signal representing a combined response from the first group of UEs.



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