RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments
RegionFed is a gradient-level federated learning framework enabling personalized retail query understanding while matching centralized accuracy with differential privacy.
RegionFed is an architecture-robust federated learning framework for personalized query understanding that operates at the gradient level, using the l2 conflict between regional and global gradients to diagnose heterogeneity and control personalization. Existing parameter-level personalized FL methods collapse on transformers, falling below 10% accuracy on T5, while RegionFed deploys unchanged on T5-Small, T5-3B, RoBERTa, and CNNs. RegionFed-Meta achieves 92.27% across Amazon ESCI, Amazon Reviews, and LEAF-FEMNIST, within 0.23 percentage points of the centralized upper bound, with epsilon-approx-0.60 differential privacy.
- Parameter-level personalized FL collapses below 10% accuracy on T5 due to tied embeddings and LayerNorm interactions.
- RegionFed uses l2 conflict between regional and global gradients to route personalization strategy and strength.
- Works as differentiable black box on T5-Small, T5-3B, RoBERTa, and CNN with zero code changes.
- RegionFed-Meta hits 92.27% on three public datasets, within 0.23pp of centralized, at epsilon-approx-0.60 DP.
- Products
- RegionFed
From the original article
1 min read at arxiv.orgRetail 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.
By Quoc H. Nguyen · arxiv.org
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