← Back to all news
arXiv AI By Quoc H. Nguyen, Ali Lafzi, Abhijeet Phatak, Siddharth Pratap Singh, Rohit Upadhyay, Yogananda Domlur Seetharama, Chittaranjan Tripathy

RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments

Read the original on arXiv AI →

RegionFed is a federated learning framework designed for personalized query understanding in retail search systems that face significant data heterogeneity across geographic regions. By operating at the gradient level and using the β1 conflict between regional and global gradients, it diagnoses heterogeneity, selects appropriate personalization strategies, and controls personalization strength without requiring code changes for different architectures. The method achieves near‑centralized performance (92.27%) on transformers and CNNs while providing differential privacy and theoretical convergence guarantees.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Aug 18

Global Federated Learning Strategies for Building Efficient Personalized Models

arXiv:2608. 15107v1 Announce Type: new Abstract: Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously.

By Seongyoon Kim
reinforcement-learningfine-tuningefficiencysafety
More like this →
arXiv Machine Learning
Aug 20

Topology-Aware Differential Privacy in Hierarchical Federated Learning

The paper introduces Fulcrum, a topology‑aware differential privacy scheme for hierarchical federated learning that allocates noise based on the size and exposure of regional aggregation groups. By deriving a closed‑form exposure dispersion metric from region structure and weights, the method optimally balances privacy and utility, achieving up to 14.84% accuracy gains on image tasks and 12.16% on text tasks at ε = 0.99 compared to uniform noise allocation. The approach ensures each participant receives noise commensurate with its actual exposure, eliminating unnecessary privacy overhead.

By Murtaza Rangwala, Richard O. Sinnott, Rajkumar Buyya
nlpsafety
More like this →
Hugging Face Trending Papers
Jun 1

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.

safety
More like this →
arXiv Machine Learning
Aug 4

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting

arXiv:2608. 01290v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) such as Chronos have demonstrated strong forecasting capabilities across domains, yet adapting them to institutionally fragmented settings, where data cannot be centralized due to regulatory, competitive, or sovereignty constraints, remains unexplored.

By Amit Sharma, Nitin Auluck, Akramul Azim
fine-tuningsafety
More like this →