Multibranch Dendritic Computing for Federated Graph Neural Networks: Maintaining Interpretability Under Privacy Constraints

Graph Neural Networks (GNNs) have demonstrated remarkable performance in modeling complex relational data. However, their deployment in privacy-sensitive domains remains challenging due to the inherent tension between privacy protection and model interpretability. This paper introduces a novel Multi-Branch Dendritic Computing framework for Federated Graph Neural Networks (MBD-FGNN) that addresses this fundamental challenge. Our approach integrates biologically-inspired dendritic computing with federated learning paradigms, enabling distributed training across decentralized graph data while preserving both privacy and interpretability. The multi-branch architecture mimics dendritic computation in biological neurons,where each branch processes patterns at different receptive field scales (1-hop, 2-hop, and 4-hop neighborhoods), providing natural model explanations through branch contribution analysis.To ensure rigorous privacy protection, we develop a branch-adaptive differential privacy mechanism with gradient clipping and calibrated Gaussian noise injection, satisfying (ϵ, δ)-differential privacy. We theoretically analyze the framework’s ability to maintain interpretation stability under differential privacy constraints and derive formal guarantees on the privacy-interpretability trade-off. Extensive experiments on benchmark graph datasets demonstrate that MBD-FGNN outperforms state-of-the-art federated GNN approaches in terms of accuracy, privacy preservation, and explanation quality. Notably, our method achieves robust explanation stability even under privacy constraints, maintaining higher explanation consistency compared to conventional approaches where explanations significantly degrade as privacy protection increases. The proposed framework opens new avenues for deploying interpretable graph learning systems in privacy-critical applications such as healthcare networks, financial transaction graphs, and social network analysis. The code and dataset are publicly available at https://github.com/ZhaoGuan-nuist/MBD-FGNN.