View source for US Patent Application 18027059. User Equipment-Coordination Set Federated for Deep Neural Networks simplified abstract

=User Equipment-Coordination Set Federated for Deep Neural Networks=


==Organization Name==

[[Google LLC]]


==Inventor(s)==

[[Jibing Wang of Mountain View CA (US)]]


[[Erik Stauffer of Mountain View CA (US)]]


==User Equipment-Coordination Set Federated for Deep Neural Networks - A simplified explanation of the abstract==


*This abstract for appeared for US patent application  number 18027059 Titled 'User Equipment-Coordination Set Federated for Deep Neural Networks'

==Simplified Explanation==

The abstract describes a method and system for coordinating user equipment (UE) in a user equipment-coordination set (UECS) to perform federated learning for deep neural networks (DNNs). The coordinating UE communicates update conditions to a subset of UEs in the UECS, indicating when to generate updated machine learning (ML) configuration information for a DNN. The coordinating UE then receives reports containing the updated ML configuration information from the subset of UEs. Each UE generates the updated ML configuration information using a training procedure and local input data. The coordinating UE applies federated learning techniques to determine a common UECS ML configuration and directs at least one UE in the subset to update its respective DNN using the common UECS ML configuration.


==Original Abstract Submitted==

Techniques and apparatuses are described for user equipment-coordination set (UECS) federated learning for deep neural networks (DNNs). A coordinating user equipment (UE) in a UECS communicates (), to at least a subset of UEs in the UECS, one or more update conditions that indicate when to generate updated ML configuration information for a respective DNN that processes UECS communications. The coordinating UE then receives () one or more reports that include the updated ML configuration information from respective UEs of at least the subset of UEs. In aspects, the respective UE generates the updated ML configuration information using a training procedure and local input data. The coordinating UE determines () a common UECS ML configuration by applying federated learning techniques to the updated ML configuration information and directs () at least one UE in the subset to update the respective DNN using the at least one common UECS ML configuration.