针对医疗时间序列稀疏数据，提出异构联邦学习系统提升预测精度。
Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction
- 设计稠密与稀疏特征张量应对数据稀疏性，支持异构模型间知识迁移。
- 在10项任务中8项优于基线，误差降低最高达94.8%。
- 适用于隐私保护下的小样本医疗数据建模，适合医疗AI研究者。
本文提出一种用于医疗稀疏时间序列预测的异构联邦学习（HFL）系统，是一种具备异构迁移能力的去中心化联邦学习算法。通过设计稠密与稀疏特征张量以处理数据源的稀疏性，并开发异构联邦学习机制，实现网络异步部分共享及模型适配选择，促进跨域知识迁移。实验表明，所提HFL在10项预测任务中的8项上达到最低预测误差，相较于基准系统，均方误差（MSE）分别降低94.8%、48.3%和52.1%。结果验证了该方法在异构领域间知识迁移的有效性，尤其在目标域较小的情况下表现突出。消融实验进一步证明了异构域选择与切换机制在保障隐私、模型安全及异构知识迁移方面的有效性。
原文摘要 · Abstract (English)
In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm with heterogeneous transfers. We design dense and sparse feature tensors to deal with the sparsity of data sources. Heterogeneous federated learning is developed to share asynchronous parts of networks and select appropriate models for knowledge transfer. Experimental results show that the proposed HFL achieves the lowest prediction error among all benchmark systems on eight out of ten prediction tasks, with MSE reduction of 94.8%, 48.3%, and 52.1% compared to the benchmark systems. These results demonstrate the effectiveness of HFL in transferring knowledge from heterogeneous domains, especially in the smaller target domain. Ablation studies then demonstrate the effectiveness of the designed mechanisms for heterogeneous domain selection and switching in predicting healthcare time series with privacy, model security, and heterogeneous knowledge transfer.
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