mlsanityData to 14 Oct 2026

Mitigating Modality Quantity and Quality Imbalance in Multimodal Online Federated Learning

arXiv 2508.11159 ↗First posted 15 Aug 2025cs.LGcited by 2

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Abstract (arXiv)

The Internet of Things (IoT) ecosystem produces massive volumes of multimodal data from diverse sources, including sensors, cameras, and microphones. With advances in edge intelligence, IoT devices have evolved from simple data acquisition units into computationally capable nodes, enabling localized processing of heterogeneous multimodal data. This evolution necessitates distributed learning paradigms that can efficiently handle such data. Furthermore, the continuous nature of data generation and the limited storage capacity of edge devices demand an online learning framework. Multimodal Online Federated Learning (MMO-FL) has emerged as a promising approach to meet these requirements. However, MMO-FL faces new challenges due to the inherent instability of IoT devices, which often results in modality quantity and quality imbalance (QQI) during data collection. In this work, we systematically investigate the impact of QQI within the MMO-FL framework and present a comprehensive theoretical analysis quantifying how both types of imbalance degrade learning performance. To address these challenges, we propose the Modality Quantity and Quality Rebalanced (QQR) algorithm, a prototype learning based method designed to operate in parallel with the training process. Extensive experiments on two real-world multimodal datasets show that the proposed QQR algorithm consistently outperforms benchmarks under modality imbalance conditions with promising learning performance.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 15 Aug 2025.

Builds on

25 of 43 references.

References · 25

  1. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  2. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 352
  3. 2021SMIL: Multimodal Learning with Severely Missing Modalitycited by 245
  4. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  5. 2022PMR: Prototypical Modal Rebalance for Multimodal Learningcited by 116
  6. 2023Provable Dynamic Fusion for Low-Quality Multimodal Datacited by 82
  7. 2022M3Care: Learning with Missing Modalities in Multimodal Healthcare Datacited by 72
  8. 2020Deep Partial Multi-View Learningcited by 71
  9. 2023FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learningcited by 60
  10. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  11. 2024Multimodal Fusion on Low-quality Data: A Comprehensive Surveycited by 49
  12. 2024Diagnosing and Re-learning for Balanced Multimodal Learningcited by 41
13 more
  1. 2023Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentationcited by 24
  2. 2024Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modalitycited by 21
  3. 2023Accelerating Hybrid Federated Learning Convergence under Partial Participationcited by 15
  4. 2022Tighter Regret Analysis and Optimization of Online Federated Learningcited by 14
  5. 2023On the Local Cache Update Rules in Streaming Federated Learningcited by 8
  6. 2023A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regretcited by 7
  7. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  8. 2023Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selectioncited by 6
  9. 2024Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learningcited by 4
  10. 2025Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Thingscited by 4
  11. 2022FedHAP: Federated Hashing with Global Prototypes for Cross-silo Retrievalcited by 2
  12. 2025Multimodal Online Federated Learning with Modality Missing in Internet of Thingscited by 2
  13. 2023Balanced Multi-modal Federated Learning via Cross-Modal Infiltrationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026AMB-DSGDN: Adaptive Modality-Balanced Dynamic Semantic Graph Differential Network for Multimodal Emotion Recognitioncited by 0
  2. 2026Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detectioncited by 0