# FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization

arXiv: https://arxiv.org/abs/2410.03070 · HTML: https://mlsanity.com/paper/2410.03070/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning](https://mlsanity.com/paper/2204.12703/) (2022)
- [Deep Partial Multi-View Learning](https://mlsanity.com/paper/2011.06170/) (2020)
- [FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning](https://mlsanity.com/paper/2308.12305/) (2023)
- [Geometric Multimodal Contrastive Representation Learning](https://mlsanity.com/paper/2202.03390/) (2022)
- [FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology](https://mlsanity.com/paper/2402.15858/) (2024)
- [FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning](https://mlsanity.com/paper/2208.02442/) (2022)
- [CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization](https://mlsanity.com/paper/2302.10413/) (2023)
- [Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning](https://mlsanity.com/paper/2209.05395/) (2022)

## Built on by (most cited first, 16 of 16)

- [MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning](https://mlsanity.com/paper/2601.22416/) (2026)
- [Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data](https://mlsanity.com/paper/2510.22880/) (2025)
- [Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data](https://mlsanity.com/paper/2602.07081/) (2026)
- [STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning](https://mlsanity.com/paper/2605.11919/) (2026)
- [PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning](https://mlsanity.com/paper/2606.09301/) (2026)
- [ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis](https://mlsanity.com/paper/2502.12180/) (2025)
- [Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity](https://mlsanity.com/paper/2605.12584/) (2026)
- [Federated Imputation under Heterogeneous Feature Spaces](https://mlsanity.com/paper/2605.16099/) (2026)
- [FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning](https://mlsanity.com/paper/2606.32016/) (2026)
- [Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity](https://mlsanity.com/paper/2608.00623/) (2026)
- [FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis](https://mlsanity.com/paper/2603.13291/) (2026)
- [Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning](https://mlsanity.com/paper/2604.23112/) (2026)
- [Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach](https://mlsanity.com/paper/2606.20382/) (2026)
- [Multimodal Federated Learning under Dual-Axis Modality Missingness](https://mlsanity.com/paper/2608.09240/) (2026)
- [MedMix: Specialization-Consistent Federated Sparse MoEs under Modality Heterogeneity](https://mlsanity.com/paper/2608.13911/) (2026)
- [Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness](https://mlsanity.com/paper/2609.28105/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant challenge arises when dealing with missing modalities in clients' datasets, where certain features or modalities are unavailable or incomplete, leading to heterogeneous data distribution. While previous studies have addressed the issue of complete-modality missing, they fail to tackle partial-modality missing on account of severe heterogeneity among clients at an instance level, where the pattern of missing data can vary significantly from one sample to another. To tackle this challenge, this study proposes a novel framework named FedMAC, designed to address multi-modality missing under conditions of partial-modality missing in FL. Additionally, to avoid trivial aggregation of multi-modal features, we introduce contrastive-based regularization to impose additional constraints on the latent representation space. The experimental results demonstrate the effectiveness of FedMAC across various client configurations with statistical heterogeneity, outperforming baseline methods by up to 26% in severe missing scenarios, highlighting its potential as a solution for the challenge of partially missing modalities in federated systems. Our source code is provided at https://github.com/nmduonggg/PEPSY