Selecting proper clients to participate in the iterative federated learning
(FL) rounds is critical to effectively harness a broad range of distributed
datasets. Existing client selection methods simply consider the variability
among FL clients with uni-modal data, however, have yet to consider clients
with multi-modalities. We reveal that traditional client selection scheme in
MFL may suffer from a severe modality-level bias, which impedes the
collaborative exploitation of multi-modal data, leading to insufficient local
data exploration and global aggregation. To tackle this challenge, we propose a
Client-wise Modality Selection scheme for MFL (CMSFed) that can comprehensively
utilize information from each modality via avoiding such client selection bias
caused by modality imbalance. Specifically, in each MFL round, the local data
from different modalities are selectively employed to participate in local
training and aggregation to mitigate potential modality imbalance of the global
model. To approximate the fully aggregated model update in a balanced way, we
introduce a novel local training loss function to enhance the weak modality and
align the divergent feature spaces caused by inconsistent modality adoption
strategies for different clients simultaneously. Then, a modality-level
gradient decoupling method is designed to derive respective submodular
functions to maintain the gradient diversity during the selection progress and
balance MFL according to local modality imbalance in each iteration. Our
extensive experiments showcase the superiority of CMSFed over baselines and its
effectiveness in multi-modal data exploitation.
As an expert commentator in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, I find the content of this article highly relevant and interesting. The concept of selecting proper clients to participate in the iterative federated learning (FL) rounds is crucial in effectively harnessing a broad range of distributed datasets. However, the existing client selection methods have only considered clients with uni-modal data and have not yet taken into account clients with multi-modalities. This limitation can lead to a severe modality-level bias, hindering the collaborative exploitation of multi-modal data.
The proposed Client-wise Modality Selection scheme for MFL (CMSFed) aims to overcome this challenge by avoiding client selection bias caused by modality imbalance. CMSFed comprehensively utilizes information from each modality to ensure a balanced participation of clients with different modalities. By selectively employing local data from different modalities in each MFL round, potential modality imbalance of the global model is mitigated.
The introduction of a novel local training loss function enhances weak modalities and aligns divergent feature spaces caused by inconsistent modality adoption strategies for different clients simultaneously. This ensures that the fully aggregated model update is approximated in a balanced way. Additionally, the modality-level gradient decoupling method maintains gradient diversity during the selection process and balances MFL according to local modality imbalance in each iteration.
The multi-disciplinary nature of this concept is evident in its integration of concepts from various fields. The use of federated learning combines elements of machine learning, distributed computing, and data privacy. The consideration of multi-modal data brings in concepts from computer vision, natural language processing, and sensor data fusion. The introduction of local training loss function and gradient decoupling method utilizes techniques from optimization and algorithm design.
In the wider field of multimedia information systems, this research contributes to the development of efficient and effective techniques for handling multi-modal data in federated learning. By addressing the modality-level bias, CMSFed enables more comprehensive data exploration and global aggregation, leading to improved performance in various multimedia applications.
In the realm of animations, artificial reality, augmented reality, and virtual realities, the proposed CMSFed scheme can enhance the training and generation of animated content by leveraging multi-modal data sources. This can result in more realistic and immersive virtual environments and augmented reality experiences. Additionally, the concept of modality-level bias mitigation can be applied to optimize the integration of different modalities in virtual and augmented reality systems, improving user interactions and overall system performance.
To conclude, the research presented in this article not only addresses an important limitation in existing client selection methods for federated learning but also showcases the potential of multi-modal data exploitation in various domains. The CMSFed scheme provides a valuable contribution to the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities by enabling the effective utilization of distributed multi-modal datasets and improving the performance of related applications and systems.