To overcome the burdens on frequent model uploads and downloads during federated learning (FL), we propose a communication-efficient re-parameterization, FedPara. Our method re-parameterizes the model's layers using low-rank matrices or tensors followed by the Hadamard product. Different from the conventional lowrank parameterization, our method is not limited to low-rank constraints. Thereby, our FedPara has a larger capacity than the low-rank one, even with the same number of parameters. It can achieve comparable performance to the original models while requiring 2.8 to 10.1 times lower communication costs than the original models, which is not achievable by the traditional low-rank parameterization. Moreover, the efficiency can be further improved by combining our method and other efficient FL techniques because our method is compatible with others. We also extend our method to a personalized FL application, pFedPara, which separates parameters into global and local ones. We show that pFedPara outperforms competing personalized FL methods with more than three times fewer parameters.
We propose high dynamic range radiance (HDR) fields, HDR-Plenoxels, that learn a plenoptic function of 3D HDR radiance fields, geometry information, and varying camera settings inherent in 2D low dynamic range (LDR) images. Our voxel-based volume rendering pipeline reconstructs HDR radiance fields with only multi-view LDR images taken from varying camera settings in an end-to-end manner and has a fast convergence speed. To deal with various cameras in real-world scenarios, we introduce a tone mapping module that models the digital in-camera imaging pipeline (ISP) and disentangles radiometric settings. Our tone mapping module allows us to render by controlling the radiometric settings of each novel view. Finally, we build a multi-view dataset with varying camera conditions, which fits our problem setting. Our experiments show that HDR-Plenoxels can express detail and high-quality HDR novel views from only LDR images with various cameras.
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