Federated learning (FL) underpins advancements in privacy-preserving
distributed computing by collaboratively training neural networks without
exposing clients' raw data. Current FL paradigms primarily focus on uni-modal
data, while exploiting the knowledge from distributed multimodal data remains
largely unexplored. Existing multimodal FL (MFL) solutions are mainly designed
for statistical or modality heterogeneity from the input side, however, have
yet to solve the fundamental issue,"modality imbalance", in distributed
conditions, which can lead to inadequate information exploitation and
heterogeneous knowledge aggregation on different modalities.In this paper, we
propose a novel Cross-Modal Infiltration Federated Learning (FedCMI) framework
that effectively alleviates modality imbalance and knowledge heterogeneity via
knowledge transfer from the global dominant modality. To avoid the loss of
information in the weak modality due to merely imitating the behavior of
dominant modality, we design the two-projector module to integrate the
knowledge from dominant modality while still promoting the local feature
exploitation of weak modality. In addition, we introduce a class-wise
temperature adaptation scheme to achieve fair performance across different
classes. Extensive experiments over popular datasets are conducted and give us
a gratifying confirmation of the proposed framework for fully exploring the
information of each modality in MFL.

通过知识从全局优势形式中转移，我们提出了一种新颖的跨模态渗透联邦学习（FedCMI）框架，有效缓解分布式条件下的模态失衡和知识异质性问题，并且在弱模态中促进局部特征开发，实现了公平的类别性能表现。