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FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis

arXiv:2603.13291v1 Announce Type: new Abstract: Multimodal sentiment analysis in federated learning environments faces significant challenges due to missing modalities, heterogeneous data distributions, and unreliable client updates. Existing federated approaches often struggle to maintain robust performance under these practical conditions. In this paper, we propose FedUAF, a unified multimodal federated learning framework that addresses these challenges through uncertainty-aware fusion and reliability-guided aggregation. FedUAF explicitly models modality-level uncertainty during local training and leverages client reliability to guide global aggregation, enabling effective learning under incomplete and noisy multimodal data. Extensive experiments on CMU-MOSI and CMU-MOSEI demonstrate that FedUAF consistently outperforms state-of-the-art federated baselines across various missing-modality patterns and Non-IID settings. Moreover, FedUAF exhibits superior robustness against noisy clien

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Xianxun Zhu, Zezhong Sun, Imad Rida, Erik Cambria, Junqi Su, Rui Wang, Hui Chen · · 1 min read · 6 views
#cs.LG #cs.AI

arXiv:2603.13291v1 Announce Type: new Abstract: Multimodal sentiment analysis in federated learning environments faces significant challenges due to missing modalities, heterogeneous data distributions, and unreliable client updates. Existing federated approaches often struggle to maintain robust performance under these practical conditions. In this paper, we propose FedUAF, a unified multimodal federated learning framework that addresses these challenges through uncertainty-aware fusion and reliability-guided aggregation. FedUAF explicitly models modality-level uncertainty during local training and leverages client reliability to guide global aggregation, enabling effective learning under incomplete and noisy multimodal data. Extensive experiments on CMU-MOSI and CMU-MOSEI demonstrate that FedUAF consistently outperforms state-of-the-art federated baselines across various missing-modality patterns and Non-IID settings. Moreover, FedUAF exhibits superior robustness against noisy clients, highlighting its potential for real-world multimodal federated applications.

Executive Summary

This article proposes FedUAF, a novel framework for multimodal federated sentiment analysis. FedUAF addresses the challenges of missing modalities, heterogeneous data distributions, and unreliable client updates through uncertainty-aware fusion and reliability-guided aggregation. The framework explicitly models modality-level uncertainty and leverages client reliability to guide global aggregation, enabling effective learning under incomplete and noisy multimodal data. Extensive experiments demonstrate FedUAF's superiority over state-of-the-art federated baselines in various settings. FedUAF's robust performance against noisy clients highlights its potential for real-world applications. This framework has significant implications for multimodal federated learning and may be particularly useful in scenarios where data is missing or unreliable.

Key Points

  • ▸ FedUAF proposes a unified multimodal federated learning framework for sentiment analysis
  • ▸ The framework addresses missing modalities, heterogeneous data distributions, and unreliable client updates
  • ▸ Uncertainty-aware fusion and reliability-guided aggregation enable effective learning under incomplete data

Merits

Robustness against Noisy Clients

FedUAF exhibits superior robustness against noisy clients, making it a valuable solution for real-world applications.

Superior Performance in Various Settings

FedUAF consistently outperforms state-of-the-art federated baselines across various missing-modality patterns and Non-IID settings.

Demerits

Limited Evaluation on Real-World Data

Although FedUAF demonstrates impressive performance in controlled experiments, its evaluation on real-world data is limited, which may raise concerns about its applicability in practical scenarios.

Complexity of the Framework

FedUAF's uncertainty-aware fusion and reliability-guided aggregation mechanisms may add complexity to the framework, which may be a barrier to adoption for some practitioners.

Expert Commentary

The proposed FedUAF framework is a significant contribution to the field of multimodal federated learning. By explicitly modeling modality-level uncertainty and leveraging client reliability, FedUAF addresses the challenges of missing modalities, heterogeneous data distributions, and unreliable client updates. The framework's robust performance against noisy clients is particularly noteworthy, as it highlights its potential for real-world applications. However, the limited evaluation on real-world data and the complexity of the framework are potential drawbacks that need to be addressed. Overall, FedUAF is a valuable solution for multimodal federated learning and may have implications for future research in this area.

Recommendations

  • ✓ Future work should focus on evaluating FedUAF on real-world data to assess its applicability in practical scenarios.
  • ✓ The framework's complexity should be addressed through simplification or optimization techniques to make it more accessible to practitioners.

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