Asymmetric Generative Adaptation for Federated Multispectral Palmprint Recognition under Spectral Heterogeneity

Multispectral palmprint recognition captures both surface ridge textures and subcutaneous vein patterns across multiple spectral bands, offering improved robustness to presentation attacks in high-security authentication scenarios. However, deploying such systems on distributed consumer-grade devices via Federated Learning (FL) introduces a critical practical barrier:Spectral Heterogeneity. Variations in local sensor hardware and ambient illumination produce severe Non-IID spectral distributions across clients, causing conventional personalized FL (pFL) to overfit on scarce local samples. To address these challenges, we present AsyGA (Asymmetric Generative Adaptation), a privacy-first pFL framework designed for heterogeneous deployment on consumer-grade devices. Each client trains a lightweight diffusion model entirely on-device to capture its local spectral profile, while only the downstream multispectral classifier is shared with the server for communication-efficient aggregation. After deployment, clients leverage these local generators to synthesize single-domain surrogate images for on-device personalization, eliminating raw biometric data exposure while minimizing communication overhead. To counteract Asymmetric Modality Collapse, a phenomenon where biased single-domain gradients overwrite representations of unseen bands during local optimization, we implement a hardware-friendly Fidelity-Preserving Structural Constraint consisting of four progressive mechanisms: (1)supervised contrastive optimization on synthetic identities for robust open-set separation; (2)feature-anchored cosine distillation against a frozen global teacher to stabilize the student embedding space; (3)batch normalization statistics locking to suppress synthetic distribution shift; and (4)targeted convolutional backbone freezing to shield globally learned multi-band filters from biased gradients while truncating backpropagation to slash on-device memory overhead. Evaluated under a strict cross-dataset open-set protocol spanning 287 million verification pairs across four spectral domains, AsyGA reduces the Equal Error Rate by over 50% against state-of-the-art FL baselines and more than doubles the True Acceptance Rate under a strict $\text{FAR}=10^{-6}$ threshold. Furthermore, the on-device personalization stage adds only 44.6% of the FedAvg training time (61.5% including one-time generator training) and executes seamlessly on a consumer-grade laptop GPU without specialized infrastructure.