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cs.LG2026
Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs
Hanlin Cai, Kai Li, Houtianfu Wang +4
Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated learning, FFT enables distr…
cs.AI2026
Nonlinearity as Rank: Generative Low-Rank Adapter with Radial Basis Functions
Yihao Ouyang, Shiwei Li, Haozhao Wang +6
Low-rank adaptation (LoRA) approximates the update of a pretrained weight matrix using the product of two low-rank matrices. However, standard LoRA follows an explicit-rank paradig…
cs.IR2026
Unbiased Rectification for Sequential Recommender Systems Under Fake Orders
Qiyu Qin, Yichen Li, Haozhao Wang +3
Fake orders pose increasing threats to sequential recommender systems by misleading recommendation results through artificially manipulated interactions, including click farming, c…