让联邦学习同时处理分类与回归任务，提升预测准确性与不确定性判断。
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression
- 本地用多输出高斯过程联合建模分类与回归任务
- 在真实数据上实现更高预测精度与更好的异常检测能力
- 适合需要多类型任务协同的医疗、金融等场景
现有联邦学习方法大多聚焦于同质任务，忽视了本地设备上的任务多样性。本文提出一种基于多输出高斯过程（MOGP）的本地多任务学习与全局联邦学习相结合的框架。MOGP可建模相关联的分类与回归任务，提供贝叶斯非参数方法以自然量化不确定性。中央服务器聚合各设备的后验分布，更新全局MOGP先验并分发至本地模型训练直至收敛。针对本地后验推断挑战，采用Pólya-Gamma增强与均值场变分推断，显著提升计算效率与收敛速度。在合成与真实数据上的实验表明，该方法在预测性能、泛化外检测、不确定性校准和收敛速率方面均表现更优，展现出在多样化应用中的潜力。代码已公开于https://github.com/JunliangLv/task_diversity_BFL。
原文摘要 · Abstract (English)
This work addresses a key limitation in current federated learning approaches, which predominantly focus on homogeneous tasks, neglecting the task diversity on local devices. We propose a principled integration of multi-task learning using multi-output Gaussian processes (MOGP) at the local level and federated learning at the global level. MOGP handles correlated classification and regression tasks, offering a Bayesian non-parametric approach that naturally quantifies uncertainty. The central server aggregates the posteriors from local devices, updating a global MOGP prior redistributed for training local models until convergence. Challenges in performing posterior inference on local devices are addressed through the Pólya-Gamma augmentation technique and mean-field variational inference, enhancing computational efficiency and convergence rate. Experimental results on both synthetic and real data demonstrate superior predictive performance, OOD detection, uncertainty calibration and convergence rate, highlighting the method's potential in diverse applications. Our code is publicly available at https://github.com/JunliangLv/task_diversity_BFL.
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