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In this paper, we propose a Similarity-Quality-based Messenger Distillation(SQMD) framework for heterogeneous asynchronous on-device healthcare analytics.By introducing a preloaded reference dataset, SQMD enables all participantdevices to distill knowledge from peers via messengers (i.e., the soft labelsof the reference dataset generated by clients) without assuming the same modelarchitecture. Furthermore, the messengers also carry important auxiliaryinformation to calculate the similarity between clients and evaluate thequality of each client model, based on which the central server creates andmaintains a dynamic collaboration graph (communication graph) to improve thepersonalization and reliability of SQMD under asynchronous conditions.Extensive experiments on three real-life datasets show that SQMD achievessuperior performance.

Heterogeneous Collaborative Learning for Personalized Healthcare Analytics via Messenger Distillation