Federated learning (FL) enables privacy-preserving collaboration among distributed clients, but practical deployments often face heterogeneous models and non-IID data, leading to degraded communication and personalization. In addition, real-world FL systems frequently encounter newly joined clients that require rapid adaptation and abnormal clients that may upload corrupted updates, further exacerbating instability and hindering global convergence. To address these challenges in image classification, we propose HFedDGHN, a Heterogeneous Federated Dynamic Graph HyperNetwork that jointly models inter-client relations and personalized parameter generation. Specifically, a graph structure learner adaptively captures client correlations to construct a dynamic collaboration graph, while a graph-convolutional hypernetwork generates model parameters for heterogeneous architectures, enabling implicit knowledge transfer without sharing local data or weights. Moreover, the framework naturally supports meta-learning-based generalization, allowing efficient adaptation to newly joined clients. Furthermore, the dynamic graph enhances robustness by isolating abnormal clients, as they tend to be excluded from most neighborhoods during adaptive graph construction. Extensive experiments across multiple benchmarks demonstrate that HFed-DGHN achieves superior accuracy compared to state-of-the-art personalized and heterogeneous FL methods, while naturally improving robustness and scalability in real-world deployments.
Yang et al. (2026) studied this question.