RASO：面向缺失模态多模态联邦学习的角色感知服务器优化方法
Role-Aware Server Optimization for Multimodal Federated Learning with Missing Modalities
做联邦学习或多人协作文本图像训练的可以看看，RASO 专门处理客户端缺模态的情况，比按可用性聚合更精细。
arXiv 论文提出 RASO（Role-Aware Server Optimization），解决多模态联邦学习（MFL）中客户端模态缺失时的服务器端优化问题。该方法通过预测依赖性选择贡献客户端，区分编码器与共享预测块两种角色分别处理动量更新。实验覆盖多个数据集与融合架构，显示 RASO 在模态缺失场景下的聚合效果优于基于可用性的聚合策略。论文还指出预测块动量在不同编码器更新策略下可能提升或降低性能。
Role-Aware Server Optimization for Multimodal Federated Learning with Missing Modalities
Multimodal federated learning (MFL) trains a shared model by aggregating client updates without exchanging raw data. Under missing modalities, clients adapt active modality encoders and a shared prediction block (fusion and classifier) to different input subsets. This raises two server-side questions: which clients should contribute to each component, and how should their aggregates be incorporated across rounds? We observe that an absent-modality encoder can still adapt through missing-input handling. Availability- based aggregation can therefore omit its update while retaining jointly learned prediction-block updates. Contributor selection also shapes server momentum, a weighted history of aggregated updates. We observe that prediction-block momentum can improve or reduce performance depending on the encoder update policy. We therefore propose Role-Aware Server Optimization (RASO), which coordinates contributor selection with role-specific server updates. RASO selects contributors through prediction dependence and inclusion in local optimization, retains encoder momentum, and synchronizes the shared prediction block to its current aggregate. Experiments across datasets and fusion architectures demonstrate the effectiveness of RASO under missing modalities.