arXiv:2606. 02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity.
By Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun
arXiv:2608. 15153v1 Announce Type: cross Abstract: Differentially private federated learning must balance privacy protection against model accuracy and training efficiency.
By Wenjing Wei, Alla Jammine, Farid Nait-Abdesselam
arXiv:2607. 20914v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning (PEFT) enables communication-efficient adaptation of large pretrained models on decentralized edge data, but it remains fragile under non-IID client heterogeneity.
By Shiva Raj Pokhrel, Dipsan Bhattarai, Anwar Walid
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.
arXiv:2609.05770v1 Announce Type: new
Abstract: Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see a...
By Duc Dm, Khai Le-Duc, Nguyen Do, Minh Son Hoang, Florent Draye, Thai Hoang, Hoang Phuong Dam, Jiarui Liu, Chris Ngo, Terry Jingchen Zhang, Anh Le Duc Tran, Nhat Do Minh, Minh Ngoc Le, My T. Thai, Ran Xu, Silvio Savarese, Mona Diab, Bernhard Sch\"olkopf, Zhijing Jin, Huy L. Nguyen, Daeyoung Kim
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.
By Quoc H. Nguyen, Ali Lafzi, Abhijeet Phatak, Siddharth Pratap Singh, Rohit Upadhyay, Yogananda Domlur Seetharama, Chittaranjan Tripathy