FedMGP：基于多组文本-视觉提示的个性化联邦学习
FedMGP: Personalized Federated Learning with Multi-Group Text-Visual Prompts
AI总结：
本文提出FedMGP，通过多组文本-视觉提示与动态聚合策略，在视觉语言模型中实现个性化联邦学习，有效平衡公共知识与客户端特征，以最低通信参数达到最先进性能。
摘要：
本文提出了FedMGP，一种用于视觉语言模型中个性化联邦提示学习的新范式。FedMGP为每个客户端配备多组配对的文本和视觉提示，使模型能够捕捉多样、细粒度的语义和实例级线索。引入了多样性损失以驱动每组提示专注于不同且互补的语义方面，确保各组共同覆盖更广泛的局部特征。在通信期间，FedMGP采用基于相似性引导概率采样的动态提示聚合策略：每个客户端计算其提示组与上一轮全局提示间的余弦相似度，然后通过softmax加权分布采样s个组。这种软选择机制优先聚合语义对齐的知识，同时仍能探索代表性不足的模式，有效平衡了公共知识的保留与客户端特有特征。值得注意的是，FedMGP通过将固定提示容量重新分配到多个组来保持参数效率，在所有联邦提示学习方法中以最低的通信参数实现了最先进的性能。理论分析表明，动态聚合策略通过强化共享语义并抑制客户端特有噪声，促进了鲁棒的全局表示学习。大量实验证明，FedMGP在多样化的联邦视觉语言基准上的个性化和域泛化方面均持续优于先前方法。代码将发布于https://github.com/weihao-bo/FedMGP.git。
英文摘要：
In this paper, we introduce FedMGP, a new paradigm for personalized federated prompt learning in vision-language models. FedMGP equips each client with multiple groups of paired textual and visual prompts, enabling the model to capture diverse, fine-grained semantic and instance-level cues. A diversity loss is introduced to drive each prompt group to specialize in distinct and complementary semantic aspects, ensuring that the groups collectively cover a broader range of local characteristics. During communication, FedMGP employs a dynamic prompt aggregation strategy based on similarity-guided probabilistic sampling: each client computes the cosine similarity between its prompt groups and the global prompts from the previous round, then samples s groups via a softmax-weighted distribution. This soft selection mechanism preferentially aggregates semantically aligned knowledge while still enabling exploration of underrepresented patterns effectively balancing the preservation of common knowledge with client-specific features. Notably, FedMGP maintains parameter efficiency by redistributing a fixed prompt capacity across multiple groups, achieving state-of-the-art performance with the lowest communication parameters among all federated prompt learning methods. Theoretical analysis shows that our dynamic aggregation strategy promotes robust global representation learning by reinforcing shared semantics while suppressing client-specific noise. Extensive experiments demonstrate that FedMGP consistently outperforms prior approaches in both personalization and domain generalization across diverse federated vision-language benchmarks. The code will be released on https://github.com/weihao-bo/FedMGP.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
