提出新型联邦学习框架，提升边缘设备多模态数据分类精度。
HybridVFL: Disentangled Feature Learning for Edge-Enabled Vertical Federated Multimodal Classification
- 客户端分离特征，服务端用跨模态变换融合信息
- 在HAM10000数据集上显著优于传统联邦基线
- 适合隐私敏感的边缘医疗诊断场景
垂直联邦学习（VFL）为移动健康诊断等边缘AI场景提供隐私保护方案，其中敏感的多模态数据分布在资源受限的分布式设备上。然而，标准VFL系统因特征融合方式简单而性能受限。本文提出HybridVFL框架，通过客户端特征解耦与服务端跨模态Transformer实现上下文感知融合，克服该瓶颈。在多模态HAM10000皮肤病变数据集上的系统评估表明，HybridVFL显著优于标准联邦基线，验证了先进融合机制对鲁棒、隐私保护系统的关键作用。
原文摘要 · Abstract (English)
Vertical Federated Learning (VFL) offers a privacy-preserving paradigm for Edge AI scenarios like mobile health diagnostics, where sensitive multimodal data reside on distributed, resource-constrained devices. Yet, standard VFL systems often suffer performance limitations due to simplistic feature fusion. This paper introduces HybridVFL, a novel framework designed to overcome this bottleneck by employing client-side feature disentanglement paired with a server-side cross-modal transformer for context-aware fusion. Through systematic evaluation on the multimodal HAM10000 skin lesion dataset, we demonstrate that HybridVFL significantly outperforms standard federated baselines, validating the criticality of advanced fusion mechanisms in robust, privacy-preserving systems.
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