RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments
[Uczenie maszynowe] Autorzy: Quoc H. Nguyen, Ali Lafzi, Abhijeet Phatak i 4 więcej
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…
- Kategoria arxiv
- cs.LG
- Kategoria
- Uczenie maszynowe