# GraMFedDHAR: Graph Based Multimodal Differentially Private Federated HAR

arXiv: https://arxiv.org/abs/2509.05671 · HTML: https://mlsanity.com/paper/2509.05671/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 6 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Deep ConvLSTM with self-attention for human activity decoding using wearables](https://mlsanity.com/paper/2005.00698/) (2020)
- [A Graph Federated Architecture with Privacy Preserving Learning](https://mlsanity.com/paper/2104.13215/) (2021)
- [Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition](https://mlsanity.com/paper/2411.02099/) (2024)
- [MHARFedLLM: Multimodal Human Activity Recognition Using Federated Large Language Model](https://mlsanity.com/paper/2508.01701/) (2025)

## Built on by (most cited first, 1 of 1)

- [FedMHAR: Federated Multimodal Human Activity Recognition using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2609.33492/) (2026)

## Abstract (arXiv)

Human Activity Recognition (HAR) using multimodal sensor data remains challenging due to noisy or incomplete measurements, scarcity of labeled examples, and privacy concerns. Traditional centralized deep learning approaches are often constrained by infrastructure availability, network latency, and data sharing restrictions. While federated learning (FL) addresses privacy by training models locally and sharing only model parameters, it still has to tackle issues arising from the use of heterogeneous multimodal data and differential privacy requirements. In this article, a Graph-based Multimodal Federated Learning framework, GraMFedDHAR, is proposed for HAR tasks. Diverse sensor streams such as a pressure mat, depth camera, and multiple accelerometers are modeled as modality-specific graphs, processed through residual Graph Convolutional Neural Networks (GCNs), and fused via attention-based weighting rather than simple concatenation. The fused embeddings enable robust activity classification, while differential privacy safeguards data during federated aggregation. Experimental results show that the proposed MultiModalGCN model outperforms the baseline MultiModalFFN, with up to 2 percent higher accuracy in non-DP settings in both centralized and federated paradigms. More importantly, significant improvements are observed under differential privacy constraints: MultiModalGCN consistently surpasses MultiModalFFN, with performance gaps ranging from 7 to 13 percent depending on the privacy budget and setting. These results highlight the robustness of graph-based modeling in multimodal learning, where GNNs prove more resilient to the performance degradation introduced by DP noise.