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.
Federated Learning and Multimodal Data: Expanding Possibilities
Federated learning (FL) has been a driving force behind advancements in privacy-preserving distributed computing. By allowing neural networks to be trained collaboratively without exposing clients’ raw data, FL addresses the privacy concerns associated with centralized data storage and processing. While current FL paradigms have primarily focused on uni-modal data, there is immense potential in exploiting the knowledge from distributed multimodal data. This potential, however, remains largely unexplored.
In this context, the authors of this paper propose a novel framework called Cross-Modal Infiltration Federated Learning (FedCMI). The aim of FedCMI is to effectively address the issue of “modality imbalance” in distributed conditions, which can hinder information exploitation and knowledge aggregation across different modalities. Existing multimodal FL (MFL) solutions have mainly focused on statistical or modality heterogeneity from the input side, but have not adequately tackled the fundamental issue of modality imbalance.
To alleviate modality imbalance and knowledge heterogeneity, the FedCMI framework leverages knowledge transfer from the global dominant modality. This transfer of knowledge helps to ensure that the weak modalities benefit from the information available in the dominant modality, without solely imitating its behavior. The framework achieves this through the two-projector module, which integrates knowledge from the dominant modality while still promoting local feature exploitation in the weak modality.
Furthermore, the authors introduce a class-wise temperature adaptation scheme to achieve fair performance across different classes. This addresses another challenge in MFL, where certain classes may have more representation or importance in one modality compared to others.
The proposed FedCMI framework is validated through extensive experiments over popular datasets, which demonstrate its ability to fully explore the information of each modality in MFL. These results provide a gratifying confirmation of the framework’s effectiveness and pave the way for further advancements in the field.
Multi-Disciplinary Nature and Connections to Multimedia Information Systems
The concepts discussed in this article have multi-disciplinary implications and connections to various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The integration of multimodal data and federated learning has the potential to enhance multimedia information retrieval and analysis systems.
Incorporating multiple modalities such as text, images, audio, and video opens up new avenues for understanding and extracting information from multimedia content. The FedCMI framework addresses the challenges of modality imbalance and knowledge heterogeneity, which are crucial for effective utilization of multimodal data in information systems.
Moreover, this research contributes to the wider field of artificial reality, augmented reality, and virtual realities. By effectively exploiting distributed multimodal data, FL frameworks like FedCMI can potentially enhance the realism, interactivity, and immersion of virtual and augmented reality experiences. This could lead to advancements in fields such as gaming, simulation, training, and telepresence.
Overall, the proposed FedCMI framework represents an important step towards unlocking the full potential of multimodal data in FL. The research has implications across various domains and paves the way for future advancements in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.