In practical scenarios, it is necessary to build variable-sized models to accommodate diverse resource constraints, where weight initialization serves as a crucial step preceding training. The recently introduced Learngene framework firstly learns one compact module, termed learngene, from a large well-trained model, and then transforms learngene to initialize variable-sized models. However, the existing Learngene methods provide limited guidance on transforming learngene, where transformation mechanisms are manually designed and generally lack a learnable component. Moreover, these methods only consider transforming learngene along depth dimension, thus constraining the flexibility of learngene. Motivated by these concerns, we propose a novel and effective Learngene approach termed LeTs (Learnable Transformation), where we transform the learngene module along both width and depth dimension with a set of learnable matrices for flexible variablesized model initialization. Specifically, we construct an auxiliary model comprising the compact learngene module and learnable transformation matrices, enabling both components to be trained. To meet the varying size requirements of target models, we select specific parameters from well-trained transformation matrices to adaptively transform the learngene, guided by strategies such as continuous selection and magnitude-wise selection. Extensive experiments on ImageNet-1K demonstrate that Des-Nets initialized via LeTs outperform those with 100-epoch from scratch training after only 1 epoch tuning. When transferring to downstream image classification tasks, LeTs achieves better results while outperforming from scratch training after about 10 epochs within a 300-epoch training schedule.

Vision Transformers (ViTs) are widely used in a variety of applications, while they usually have a fixed architecture that may not match the varying computational resources of different deployment environments. Thus, it is necessary to adapt ViT architectures to devices with diverse computational overheads to achieve an accuracy-efficient trade-off. This concept is consistent with the motivation behind Learngene. To achieve this, inspired by polynomial decomposition in calculus, where a function can be approximated by linearly combining several basic components, we propose to linearly decompose the ViT model into a set of components called learngenes during element-wise training. These learngenes can then be recomposed into differently scaled, pre-initialized models to satisfy different computational resource constraints. Such a decomposition-recomposition strategy provides an economical and flexible approach to generating different scales of ViT models for different deployment scenarios. Compared to model compression or training from scratch, which require to repeatedly train on large datasets for diverse-scale models, such strategy reduces computational costs since it only requires to train on large datasets once. Extensive experiments are used to validate the effectiveness of our method: ViTs can be decomposed and the decomposed learngenes can be recomposed into diverse-scale ViTs, which can achieve comparable or better performance compared to traditional model compression and pre-training methods. The code for our experiments is available in the supplemental material.

Vision Transformers (ViTs) are widely used in a variety of applications, while they usually have a fixed architecture that may not match the varying computational resources of different deployment environments.

Moreover, the training and storage costs of such method grow linearly with the number of potential scenarios.

Vision Transformers (ViTs) are widely used in a variety of applications, while they usually have a fixed architecture that may not match the varying computational resources of different deployment environments.

In practical scenarios, it is necessary to build variable-sized models to accommodate diverse resource constraints, where weight initialization serves as a crucial step preceding training. The recently introduced Learngene framework firstly learns one compact module, termed learngene, from a large well-trained model, and then transforms learngene to initialize variable-sized models. However, the existing Learngene methods provide limited guidance on transforming learngene, where transformation mechanisms are manually designed and generally lack a learnable component. Moreover, these methods only consider transforming learngene along depth dimension, thus constraining the flexibility of learngene. Motivated by these concerns, we propose a novel and effective Learngene approach termed LeTs (Learnable Transformation), where we transform the learngene module along both width and depth dimension with a set of learnable matrices for flexible variablesized model initialization.

Vision Transformers (ViTs) are widely used in a variety of applications, while they usually have a fixed architecture that may not match the varying computational resources of different deployment environments. Thus, it is necessary to adapt ViT architectures to devices with diverse computational overheads to achieve an accuracy-efficient trade-off. This concept is consistent with the motivation behind Learngene. To achieve this, inspired by polynomial decomposition in calculus, where a function can be approximated by linearly combining several basic components, we propose to linearly decompose the ViT model into a set of components called learngenes during element-wise training. These learngenes can then be recomposed into differently scaled, pre-initialized models to satisfy different computational resource constraints.

Real-world \underline{F}ederated \underline{L}earning systems often encounter \underline{D}ynamic clients with \underline{A}gnostic and highly heterogeneous data distributions (DAFL), which pose challenges for efficient communication and model initialization. To address these challenges, we draw inspiration from the recently proposed Learngene paradigm, which compresses the large-scale model into lightweight, cross-task meta-information fragments. Learngene effectively encapsulates and communicates core knowledge, making it particularly well-suited for DAFL, where dynamic client participation requires communication efficiency and rapid adaptation to new data distributions. Based on this insight, we propose a Gene-driven parameter-efficient dynamic Federated Learning (GENE-FL) framework. First, local models perform quadratic constraints based on parameters with high Fisher values in the global model, as these parameters are considered to encapsulate generalizable knowledge. Second, we apply the strategy of parameter sensitivity analysis in local model parameters to condense the \textit{learnGene} for interaction. Finally, the server aggregates these small-scale trained \textit{learnGene}s into a robust \textit{learnGene} with cross-task generalization capability, facilitating the rapid initialization of dynamic agnostic client models. Extensive experimental results demonstrate that GENE-FL reduces \textbf{4 $\times$} communication costs compared to FEDAVG and effectively initializes agnostic client models with only about \textbf{9.04} MB.