# Multimodal Federated Learning With Missing Modalities through Feature Imputation Network

arXiv: https://arxiv.org/abs/2505.20232 · HTML: https://mlsanity.com/paper/2505.20232/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [Multimodal Federated Learning via Contrastive Representation Ensemble](https://mlsanity.com/paper/2302.08888/) (2023)
- [Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge](https://mlsanity.com/paper/2101.07511/) (2021)
- [FedMultimodal: A Benchmark For Multimodal Federated Learning](https://mlsanity.com/paper/2306.09486/) (2023)
- [AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving](https://mlsanity.com/paper/2302.08646/) (2023)
- [Medical Vision Language Pretraining: A survey](https://mlsanity.com/paper/2312.06224/) (2023)
- [Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality](https://mlsanity.com/paper/2401.13898/) (2024)
- [Multimodal Federated Learning in Healthcare: a Review](https://mlsanity.com/paper/2310.09650/) (2023)
- [Towards Multi-modal Transformers in Federated Learning](https://mlsanity.com/paper/2404.12467/) (2024)
- [Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection](https://mlsanity.com/paper/2402.05294/) (2024)
- [CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalities](https://mlsanity.com/paper/2407.08648/) (2024)

## Built on by (most cited first, 8 of 8)

- [Probabilistic Feature Imputation and Uncertainty-Aware Multimodal Federated Aggregation](https://mlsanity.com/paper/2604.12970/) (2026)
- [Federated Imputation under Heterogeneous Feature Spaces](https://mlsanity.com/paper/2605.16099/) (2026)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)
- [CLARGA: Multimodal Graph Representation Learning over Arbitrary Sets of Modalities](https://mlsanity.com/paper/2512.11901/) (2025)
- [Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models](https://mlsanity.com/paper/2605.12517/) (2026)
- [ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities](https://mlsanity.com/paper/2607.06633/) (2026)
- [FedDP-PALD: A Privacy-Preserving Federated Latent Diffusion Framework with Prototype Aggregation for Medical Data Synthesis](https://mlsanity.com/paper/2607.16300/) (2026)
- [Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness](https://mlsanity.com/paper/2609.28105/) (2026)

## Abstract (arXiv)

Multimodal federated learning holds immense potential for collaboratively training models from multiple sources without sharing raw data, addressing both data scarcity and privacy concerns, two key challenges in healthcare. A major challenge in training multimodal federated models in healthcare is the presence of missing modalities due to multiple reasons, including variations in clinical practice, cost and accessibility constraints, retrospective data collection, privacy concerns, and occasional technical or human errors. Previous methods typically rely on publicly available real datasets or synthetic data to compensate for missing modalities. However, obtaining real datasets for every disease is impractical, and training generative models to synthesize missing modalities is computationally expensive and prone to errors due to the high dimensionality of medical data. In this paper, we propose a novel, lightweight, low-dimensional feature translator to reconstruct bottleneck features of the missing modalities. Our experiments on three different datasets (MIMIC-CXR, NIH Open-I, and CheXpert), in both homogeneous and heterogeneous settings consistently improve the performance of competitive baselines. The code and implementation details are available at: https://github.com/bhattarailab/FedFeatGen