Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging

Snapshot compressive imaging emerges as a promising technology for acquiring real-world hyperspectral signals. It uses an optical encoder and compressively produces the 2D measurement, followed by which the 3D hyperspectral data can be retrieved via training a deep reconstruction network. Existing reconstruction models are trained with a single hardware instance, whose performance is vulnerable to hardware perturbation or replacement, demonstrating an overfitting issue to the physical configuration. This defect limits the deployment of pre-trained models since they would suffer from large performance degradation when are assembled to unseen hardware. To better facilitate the reconstruction model with new hardware, previous efforts resort to centralized training by collecting multi-hardware and data, which is impractical when dealing with proprietary assets among institutions. In light of this, federated learning (FL) has become a feasible solution to enable cross-hardware cooperation without breaking privacy. However, the naive FedAvg is subject to client drift upon data heterogeneity owning to the hardware inconsistency. In this work, we tackle this challenge by marrying prompt tuning with FL to snapshot compressive imaging for the first time and propose an federated hardware-prompt learning (FedHP) method. Rather than mitigating the client drift by rectifying the gradients, which only takes effect on the learning manifold but fails to touch the heterogeneity rooted in the input data space, the proposed FedHP globally learns a hardware-conditioned prompter to align the data distribution, which serves as an indicator of the data inconsistency stemming from different pre-defined coded apertures. Extensive experiments demonstrate that the proposed method well coordinates the pre-trained model to indeterminate hardware configurations.