US 12,237,947 B2
Federated learning for classifiers and autoencoders for wireless communication
June Namgoong, San Diego, CA (US); Taesang Yoo, San Diego, CA (US); Naga Bhushan, San Diego, CA (US); Pavan Kumar Vitthaladevuni, San Diego, CA (US); Jay Kumar Sundararajan, San Diego, CA (US); Wanshi Chen, San Diego, CA (US); Krishna Kiran Mukkavilli, San Diego, CA (US); Hwan Joon Kwon, San Diego, CA (US); Alexandros Manolakos, Escondido, CA (US); and Tingfang Ji, San Diego, CA (US)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Appl. No. 18/004,839
Filed by QUALCOMM Incorporated, San Diego, CA (US)
PCT Filed Aug. 17, 2021, PCT No. PCT/US2021/071209
§ 371(c)(1), (2) Date Jan. 9, 2023,
PCT Pub. No. WO2022/040678, PCT Pub. Date Feb. 24, 2022.
Claims priority of application No. 20200100499 (GR), filed on Aug. 18, 2020.
Prior Publication US 2023/0261909 A1, Aug. 17, 2023
Int. Cl. H04L 25/02 (2006.01); G06N 3/0455 (2023.01); H04L 25/03 (2006.01)
CPC H04L 25/0254 (2013.01) [G06N 3/0455 (2023.01); H04L 25/03171 (2013.01)] 30 Claims
OG exemplary drawing
 
1. A method of wireless communication performed by a client, comprising:
selecting, based at least in part on a classifier, an autoencoder of a set of autoencoders to be used for encoding an observed wireless communication vector to generate a latent vector; and
transmitting the latent vector and an indication of the autoencoder.