用双提示词和跨模态融合实现个性化联邦学习，提升异构数据下的模型性能。
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion
- 采用视觉与语言双提示词，分别捕捉全局共享知识和客户端特有特征。
- 跨融合模块自适应整合多级提示，生成符合本地数据分布的个性化表征。
- 在九个异构数据集上优于当前最佳方法，适合个性化需求强的联邦学习场景。
联邦学习（FL）允许在不共享本地数据的前提下跨分散客户端协作训练模型，但面临数据、计算和通信异构性的挑战。预训练视觉-语言模型（VLMs）凭借强大的泛化能力及通过提示词进行轻量级微调的特性，提供了一种有前景的解决方案。然而，现有的联邦提示学习方法仅依赖文本提示，忽视了标签域分布的变化。本文提出一种基于双提示学习与跨融合的个性化联邦学习框架pFedDC。每个客户端在视觉和语言模态上维护全局与本地提示：全局提示捕获联邦共享的通用知识，本地提示编码客户端特有的语义与领域特征。同时，设计跨融合模块以自适应整合不同层级的提示，使模型生成与各客户端独特数据分布对齐的个性化表示。在包含多种异构性类型的九个数据集上的大量实验表明，pFedDC始终优于当前最优方法。
原文摘要 · Abstract (English)
Federated learning (FL) enables collaborative model training across decentralized clients without sharing local data, but is challenged by heterogeneity in data, computation, and communication. Pretrained vision-language models (VLMs), with their strong generalization and lightweight tuning via prompts, offer a promising solution. However, existing federated prompt-learning methods rely only on text prompts and overlook joint label-domain distribution shifts. In this paper, we propose a personalized FL framework based on dual-prompt learning and cross fusion, termed pFedDC. Specifically, each client maintains both global and local prompts across vision and language modalities: global prompts capture common knowledge shared across the federation, while local prompts encode client-specific semantics and domain characteristics. Meanwhile, a cross-fusion module is designed to adaptively integrate prompts from different levels, enabling the model to generate personalized representations aligned with each client's unique data distribution. Extensive experiments across nine datasets with various types of heterogeneity show that pFedDC consistently outperforms state-of-the-art methods.
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