论文标题
通过Messenger蒸馏，用于个性化医疗分析的异质协作学习
Heterogeneous Collaborative Learning for Personalized Healthcare Analytics via Messenger Distillation
论文作者
论文摘要
在本文中，我们提出了一种基于相似性的质量蒸馏（SQMD）框架，用于异质异步医疗保健分析。通过引入预加载的参考数据集，SQMD使所有参与者设备都可以通过Messenger（即客户生成的参考数据集的软标签）从同行中提取知识，而无需假设相同的模型体系结构。此外，Messenger还提供重要的辅助信息，以计算客户之间的相似性并评估每个客户端模型的质量，基于中央服务器在此基础上创建和维护动态协作图（通信图），以提高在异步条件下SQMD的个性化和可靠性。在三个现实生活中的数据集上进行了广泛的实验表明，SQMD达到了出色的性能。
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 participant devices to distill knowledge from peers via messengers (i.e., the soft labels of the reference dataset generated by clients) without assuming the same model architecture. Furthermore, the messengers also carry important auxiliary information to calculate the similarity between clients and evaluate the quality of each client model, based on which the central server creates and maintains a dynamic collaboration graph (communication graph) to improve the personalization and reliability of SQMD under asynchronous conditions. Extensive experiments on three real-life datasets show that SQMD achieves superior performance.
