Author

Yu-Qing Li

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Conference Open access 2026

Federated LoRA Fine-Tuning with Pipelined Error-Mitigated Aggregation and Matrix-Wise Freezing

iFLoRA is proposed, an improved Federated LoRA fine-tuning system for LLMs featuring pipelined error-mitigated model aggregation and adaptive matrix-wise parameter freezing and can improve time-to-target by 2.17-8.48 × than state-of-the-art methods.

Haoran Wang, Xiong Wang, Yuqing Li et al. · Annual Meeting of the Associ... · 1 citation

#artificial intelligence Preprint Aug 2026

CoRe-MoE: Compact Reusable MoE for Continual Multimodal Instruction Tuning

CoRe-MoE is proposed, a Compact Reusable MoE framework for parameter-efficient continual multimodal instruction tuning that improves final average performance over the strongest competing baseline by up to 5.90 points, while using less than 1% of the trainable parameters required by sequential LoRA for later tasks.

Run-Ze Liu, Nai-Bin Gu, Ming-Xu Ai et al. · 0 citations