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cs.LG2025
Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking
Xingchen Wang, Feijie Wu, Chenglin Miao +5
Split Federated Learning (SFL) has emerged as an efficient alternative to traditional Federated Learning (FL) by reducing client-side computation through model partitioning. Howeve…
cs.LG2025
Towards Universal Debiasing for Language Models-based Tabular Data Generation
Tianchun Li, Tianci Liu, Xingchen Wang +4
Large language models (LLMs) have achieved promising results in tabular data generation. However, inherent historical biases in tabular datasets often cause LLMs to exacerbate fair…
cs.CL2025
Towards Federated RLHF with Aggregated Client Preference for LLMs
Feijie Wu, Xiaoze Liu, Haoyu Wang +3
Reinforcement learning with human feedback (RLHF) fine-tunes a pretrained large language model (LLM) using user preference data, enabling it to generate content aligned with human…