notesum.ai

Published at April 29

Federated Model Heterogeneous Matryoshka Representation Learning

NeurIPS

Released Date: April 29, 2024

Authors: Liping Yi¹, Han Yu², Chao Ren², Gang Wang¹, xiaoguang Liu¹, Xiaoxiao Li³

Aff.: ¹College of Computer Science, TMCC, SysNet, DISSec, GTIISC, Nankai University, China; ²College of Computing and Data Science, Nanyang Technological University, Singapore; ³Department of Electrical and Computer Engineering, The University of British Columbia, Canada

Arxiv: https://openreview.net/pdf/15aa7c10393ea8ad34b2f2a9dbae1ebf87744dc5.pdf

FL Setting	N=10, C=100%		N=50, C=20%		N=100, C=10%
Method	CIFAR-10	CIFAR-100	CIFAR-10	CIFAR-100	CIFAR-10	CIFAR-100
Standalone	96.53	72.53	95.14	62.71	91.97	53.04
LG-FedAvg [24]	96.30	72.20	94.83	60.95	91.27	45.83
FD [19]	96.21	-	-	-	-	-
FedProto [41]	96.51	72.59	95.48	62.69	92.49	53.67
FML [38]	30.48	16.84	-	21.96	-	15.21
FedKD [43]	80.20	53.23	77.37	44.27	73.21	37.21
FedAPEN [34]	-	-	-	-	-	-
FedMRL	96.63	74.37	95.70	66.04	95.85	62.15
FedMRL-Best B.	0.10	1.78	0.22	3.33	3.36	8.48
FedMRL-Best S.C.B.	16.43	21.14	18.33	21.77	22.64	24.94