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Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning

Jieming Bian, Lei Wang, Jie Xu

TL;DR

This work tackles the inefficiency of uniform training across modalities in multimodal federated learning by introducing FlexMod, which uses prototype-based encoder quality, Shapley-value modality importance, and deep reinforcement learning (DDPG) to adaptively allocate training resources among modality combinations. It formulates a local optimization problem and a principled training-ordering strategy, yielding faster convergence and competitive accuracy across three real-world datasets. The approach advances practical MFL for resource-constrained devices by prioritizing critical modalities and scheduling training dynamically, with a planned extension to partial participation in future work.

Abstract

Federated Learning (FL) is a distributed machine learning approach that enables devices to collaboratively train models without sharing their local data, ensuring user privacy and scalability. However, applying FL to real-world data presents challenges, particularly as most existing FL research focuses on unimodal data. Multimodal Federated Learning (MFL) has emerged to address these challenges, leveraging modality-specific encoder models to process diverse datasets. Current MFL methods often uniformly allocate computational frequencies across all modalities, which is inefficient for IoT devices with limited resources. In this paper, we propose FlexMod, a novel approach to enhance computational efficiency in MFL by adaptively allocating training resources for each modality encoder based on their importance and training requirements. We employ prototype learning to assess the quality of modality encoders, use Shapley values to quantify the importance of each modality, and adopt the Deep Deterministic Policy Gradient (DDPG) method from deep reinforcement learning to optimize the allocation of training resources. Our method prioritizes critical modalities, optimizing model performance and resource utilization. Experimental results on three real-world datasets demonstrate that our proposed method significantly improves the performance of MFL models.

Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning

TL;DR

This work tackles the inefficiency of uniform training across modalities in multimodal federated learning by introducing FlexMod, which uses prototype-based encoder quality, Shapley-value modality importance, and deep reinforcement learning (DDPG) to adaptively allocate training resources among modality combinations. It formulates a local optimization problem and a principled training-ordering strategy, yielding faster convergence and competitive accuracy across three real-world datasets. The approach advances practical MFL for resource-constrained devices by prioritizing critical modalities and scheduling training dynamically, with a planned extension to partial participation in future work.

Abstract

Federated Learning (FL) is a distributed machine learning approach that enables devices to collaboratively train models without sharing their local data, ensuring user privacy and scalability. However, applying FL to real-world data presents challenges, particularly as most existing FL research focuses on unimodal data. Multimodal Federated Learning (MFL) has emerged to address these challenges, leveraging modality-specific encoder models to process diverse datasets. Current MFL methods often uniformly allocate computational frequencies across all modalities, which is inefficient for IoT devices with limited resources. In this paper, we propose FlexMod, a novel approach to enhance computational efficiency in MFL by adaptively allocating training resources for each modality encoder based on their importance and training requirements. We employ prototype learning to assess the quality of modality encoders, use Shapley values to quantify the importance of each modality, and adopt the Deep Deterministic Policy Gradient (DDPG) method from deep reinforcement learning to optimize the allocation of training resources. Our method prioritizes critical modalities, optimizing model performance and resource utilization. Experimental results on three real-world datasets demonstrate that our proposed method significantly improves the performance of MFL models.
Paper Structure (27 sections, 2 theorems, 26 equations, 11 figures, 1 table)

This paper contains 27 sections, 2 theorems, 26 equations, 11 figures, 1 table.

Table of Contents

  1. Introduction
  2. A Motivational Study
  3. Our Contribution
  4. Related Works
  5. Federated Learning
  6. Multimodal Federated Learning
  7. Federated Learning with Prototype
  8. Federated Learning with Deep Reinforcement Learning
  9. Preliminaries
  10. System Model
  11. Problem Formulation
  12. Proposed Method
  13. Quantifying Modality Combination Factors
  14. Encoder Quality
  15. Importance
  16. ...and 12 more sections

Key Result

Theorem 1

Suppose Assumptions ass1--ass2 hold. Starting from the same initial global model $\Theta^r$, the difference between the real entire global model $\Theta^{r+1}$ and the optimal global model $\hat{\Theta}^{r+1}$ at round $r+1$ is bounded as follows: where $\eta$ is the learning rate, and $|\mathcal{C}_{\boldsymbol{s}_e}|$ represents the number of modalities selected to update in the local update $e

Figures (11)

  • Figure 1: Modality-specific encoders extract features from each modality data, which are then input into a header encoder. Given the differing formats and varying importance of modalities, training of modality encoders should be unequal.
  • Figure 2: Motivational Study on the UCI-HAR Dataset. Figure (a) shows the classification accuracy on the test dataset in a federated learning setting. Figure (b) displays the training time required for 20,000 updates on a single GPU (Nvidia L4 or T4), measured in seconds.
  • Figure 3: The weight can impact not only the allocation decision for a given round but also the final convergence performance. Figure (a) shows that at a given round, if beta varies between 0, 0.5, and 1, the combination allocation decision could be different. Figure (b) shows that with different values of fixed weight, the final model achieves different performance levels.
  • Figure 4: Convergence performances on UCI-HAR
  • Figure 5: KU-HAR Non-IID
  • ...and 6 more figures

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Corollary 1
  • proof