FedGeo: Fine-Tuning-Free Federated Personalization via Geometric Stability

Personalized federated learning addresses performance degradation under label distribution skew. Fine-tuning-based methods require local optimization for unseen clients. Meta-learning-based generation can avoid these updates, but implicit representations and unconstrained generators may produce unstable parameters and lower unseen-client accuracy. We propose Fine-Tuning-Free **Fed**erated Personalization via **Geo**metric Stability (**FedGeo**), a framework for generalization to unseen clients. Given a label prior estimated from local labeled support, FedGeo uses a geometry-preserving encoder and a spectrally constrained affine generator to produce personalized modulation parameters without local gradient updates. Together, distance preservation and bounded parameter sensitivity provide geometric stability. We establish generalization error bounds and conditional convergence guarantees under federated label-skewed distributions. Experiments demonstrate strong performance for both seen and unseen clients across multiple benchmarks. The anonymous source code is available at [https://anonymous.4open.science/r/FedGeo](https://anonymous.4open.science/r/FedGeo).