RegionFed：联邦学习实现零售个性化查询理解
RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments
RegionFed解决了联邦学习中transformer模型崩溃问题，在T5-Small到T5-3B等多种架构上实现零代码部署，准确率接近集中式模型。
RegionFed是一种架构鲁棒的联邦学习框架，专为解决零售环境中地域数据异构性问题。该框架在梯度层面操作，解决了传统参数级方法在T5等现代transformer模型上准确率低于10%的崩溃问题。在Amazon ESCI等三个数据集上，RegionFed-Meta达到92.27%准确率，接近集中式模型92.04%的上限，同时提供(ε≈0.60)-差分隐私。
RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments
Retail search systems serve diverse geographic regions with distinct query patterns, vocabularies, and product preferences, creating significant data heterogeneity that challenges both privacy-preserving training and model personalization. Federated learning offers a natural solution for privacy, but standard FL methods produce global models that sacrifice regional performance, while existing personalized FL approaches operate at the parameter level and catastrophically collapse on modern transformers (below 10\% accuracy on T5) due to tied embeddings and LayerNorm interactions. We introduce RegionFed, an \textit{architecture-robust} federated learning framework that sidesteps this failure by operating entirely at the gradient level. RegionFed uses the $\ell_2$ conflict between regional and global gradients as a unified signal that (i) diagnoses heterogeneity, (ii) routes each region to the cheapest sufficient personalization strategy, and (iii) adaptively controls personalization strength. Because it treats models as differentiable black boxes, RegionFed deploys on T5-Small, T5-3B, RoBERTa, and CNN with zero code changes, providing large gains on transformers (where parameter-level methods collapse) and consistent improvements on CNNs. Across three public datasets (Amazon ESCI, Amazon Reviews, LEAF-FEMNIST) and four architectures, RegionFed-Meta achieves 92.27\%, closing the gap to the privacy-violating centralized upper bound (Centralized + Regional Weighting: 92.04\%, $Δ$=0.23pp, within 1$σ$) while providing $(ε{\approx}0.60)$-differential privacy and $\mathcal{O}(1/\sqrt{T})$ convergence.
