Existing federated intelligence paradigms primarily focus on how models are trained or aggregated, while overlooking a more fundamental question: \textbf{what information should leave the client?} Existing methods often share model updates that users cannot easily inspect, or data representations that may reveal sensitive information. Moreover, generating these representations can be computationally expensive for resource-constrained clients. We observe that image classification does not require all the visual details needed to reconstruct the original image. This suggests that clients could share less information while preserving task performance. However, learning what to preserve is challenging when the server cannot access raw client data. To address this challenge, we propose \textbf{FedPix}, which learns to share compact, visually inspectable images instead of model updates or intermediate features. The key idea is to use the shared representations themselves to learn what information should be preserved and what can be discarded. By balancing task utility and reconstruction resistance, FedPix enables the server to improve the representations without accessing raw client data, while keeping client-side computation lightweight. Extensive experiments on BloodMNIST, CIFAR-10, FashionMNIST, and MNIST demonstrate that FedPix achieves strong reconstruction resistance, with reconstruction PSNR as low as 6.87~dB and synthetic PSNR as low as 6.74~dB. Meanwhile, FedPix reduces client runtime by up to 96.0\% (from 1223~s to 49~s on CIFAR-10), while maintaining competitive classification accuracy of 99.18\% on MNIST, 89.97\% on FashionMNIST, 82.24\% on BloodMNIST, and 65.63\% on CIFAR-10.

翻译：暂无翻译