HyFLM: A Hypernetwork-Based Federated Learning with Multidimensional Trajectory Optimization on Diffusion Paths

The effective training of large-scale distributed deep learning models has become an active and emerging research area in recent years. Federated learning (FL) can address those challenges by training global models through parameter exchange of client models rather than raw data sharing, thereby preserving security and communication efficiency. However, conventional linear aggregation approaches in FL neglect heterogeneous client models and non-IID data. This often results in inter-layer information imbalance and feature-space misalignment, leading to low overall accuracy and unstable training. To overcome these limitations, we propose HyFLM, a personalized federated learning framework that maximizes performance with Multidimensional Trajectory Optimization theory (MTO) on diffusion paths. HyFLM extends a diffusion-based FL framework by encoding client–parameter dependencies with a diffusion model and precisely controlling dimension-specific paths, thereby generating personalized weights that reflect both the data complexity and the resource constraints of each client. In addition, a lightweight hypernetwork generates client-specific adapters or weights to further enhance personalization. Extensive experiments on multiple benchmarks demonstrate that HyFLM consistently outperforms major baselines in terms of both accuracy and communication efficiency, achieving faster convergence and higher accuracy. Furthermore, ablation studies verify the contribution of MAC to convergence acceleration, confirming that HyFLM is an effective and practical personalized FL paradigm for heterogeneous client models.