Federated Learning with Partial Model Personalization. (arXiv:2204.03809v1 [cs.LG])

We consider two federated learning algorithms for training partially
personalized models, where the shared and personal parameters are updated
either simultaneously or alternately on the devices. Both algorithms have been
proposed in the literature, but their convergence properties are not fully
understood, especially for the alternating variant. We provide convergence
analyses of both algorithms in the general nonconvex setting with partial
participation and delineate the regime where one dominates the other. Our
experiments on real-world image, text, and speech datasets …

arxiv federated learning learning personalization