通过自适应调节更新强度，提升联邦学习的泛化能力。
Federated Learning for Feature Generalization with Convex Constraints
- 基于全局模型参数强度动态调整更新幅度，避免过拟合。
- 在多个数据异构场景下实现最优性能，显著提升特征迁移能力。
- 适合数据分布差异大的联邦学习场景，尤其关注泛化效果的研究者。
联邦学习（FL）常因客户端数据异构导致泛化能力差，本地模型易过拟合局部数据分布，即使可迁移特征也会在聚合过程中失真。为此，我们提出FedCONST，一种根据全局模型参数强度自适应调节更新幅度的方法，防止对已充分学习的参数过度强调，同时强化未充分发展的参数。具体而言，FedCONST采用线性凸约束，确保训练稳定并保留本地学习的泛化能力。梯度信噪比（GSNR）分析进一步验证了其在提升特征可迁移性和鲁棒性方面的有效性。结果表明，FedCONST有效对齐了本地与全局目标，缓解过拟合，促进多样化联邦环境下的更强泛化，达到当前最优性能。
原文摘要 · Abstract (English)
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 parameter strength of the global model. 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 the effectiveness of FedCONST 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.
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