5 papers
Distributed Prediction under Heterogeneity with Unidentifiable Parameter
Erbo Li, Zhaojun Hu, Ting Wei +2
Predicting a response based on covariates is a fundamental problem in statistics and machine learning. However, profound difficulties arise when the underlying low-dimensional stru…
Range Penalization: Theoretical Insights with Applications in Federated Learning
Yiyuan She, Zhaojun Hu, Yifan Sun
This paper introduces range regularization for federated learning with linear systematic components to enhance statistical accuracy and induce cross-client regularity conducive to…
Federated Reasoning Distillation Framework with Model Learnability-Aware Data Allocation
Wei Guo, Siyuan Lu, Xiangdong Ran +8
Data allocation plays a critical role in federated large language model (LLM) and small language models (SLMs) reasoning collaboration. Nevertheless, existing data allocation metho…
H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
Wei Guo, Siyuan Lu, Yiqi Tong +5
Different from existing federated fine-tuning (FFT) methods for foundation models, hybrid heterogeneous federated fine-tuning (HHFFT) is an under-explored scenario where clients ex…
Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
Wei Guo, Yiyang Duan, Zhaojun Hu +7
In vertical federated learning (VFL), multiple enterprises address aligned sample scarcity by leveraging massive locally unaligned samples to facilitate collaborative learning. How…