Federated learning (FL) often struggles with generalization due to heterogeneous client data. Local models are prone to overfitting their local data distributions, and even transferable features can be distorted during aggregation. To address these challenges, we propose FedCONST, an approach that adaptively modulates update magnitudes based on the global model’s parameter strength. This prevents over-emphasizing well-learned parameters while reinforcing underdeveloped ones. Specifically, FedCONST employs linear convex constraints to ensure training stability and preserve locally learned generalization capabilities during aggregation. A Gradient Signal-to-Noise Ratio (GSNR) analysis further validates FedCONST's effectiveness in enhancing feature transferability and robustness. As a result, FedCONST effectively aligns local and global objectives, mitigating overfitting and promoting stronger generalization across diverse FL environments, achieving state-of-the-art performance.

Traditional tabular classifiers provide explainable decision-making with interpretable features (concepts). However, using their explainability in vision tasks has been limited due to the pixel representation of images. In this paper, we design Img2Tabs that classify images by concepts to harness the explainability of tabular classifiers. Img2Tabs encode image pixels into tabular features by StyleGAN inversion. Since not all of the resulting features are class-relevant or interpretable due to their generative nature, the Img2Tab classifier should automatically discover class-relevant concepts from the StyleGAN features. Thus, we propose a novel algorithm using the Wasserstein-1 metric to quantify class-relevancy and interpretability simultaneously. By this method of concept visualization, we quantitatively investigate whether important features extracted by tabular classifiers are class-relevant concepts. Consequently, we determine the most effective classifier for Img2Tabs in terms of discovering class-relevant concepts automatically from StyleGAN features. In evaluations, we demonstrate concept-based explanations through importance and visualization. Img2Tab achieves top-1 accuracy on par with CNN classifiers and deep feature learning baselines. Additionally, we show that users can interactively debug Img2Tab classifier to prevent erroneous decision-making from data bias without sacrificing accuracy. The source and demo code for Img2Tab are available at https://github.com/songsnim/Img2Tab_pytorch

The ability to rapidly adapt to unseen tasks is a fundamental objective in few-shot learning. Recent advances in optimization-based meta-learning have enhanced adaptability by learning sharable prior knowledge across tasks with just a few gradient descent steps. However, we argue that this shared prior knowledge can exert an imbalanced influence on individual samples within tasks, potentially resulting in a broad loss distribution where samples closely aligned with the prior knowledge exhibit low loss values, while others display high loss values. Furthermore, our experiments show that gradients computed as the average from a broad loss distribution tend to be non-representative and low, leading to poor generalization performance since the contribution of high-loss samples is diminished by low-loss samples. To address this, we propose a novel meta-learning method that arbitrates gradient norms based on sample-aware information during task adaptation. Specifically, we first normalize the gradient vector to reduce the imbalanced influence of prior knowledge on individual samples. Subsequently, the Arbiter, a learnable network, dynamically scales the current gradient norm by analyzing the relationship between original gradient norms and weight norms, which indicates the model’s sensitivity and complexity to each sample. In this way, the proposed method, Meta-learning with Gradient Norm Arbitration (Meta-GNA), improves generalization performance by preserving more representative and higher gradients that adequately reflect high-loss samples, which are distantly aligned with prior knowledge. Experimental results show that Meta-GNA improves performance in few-shot classification, particularly in cross-domain scenarios where the imbalance in prior knowledge across samples is more pronounced.