Jingguang Li — accepted AI-conference papers

2026

Developmental Federated Tuning: A Cognitive-Inspired Paradigm for Efficient LLM Adaptation

Yebo Wu, Jingguang Li, Zhijiang Guo, Li Li

ICLR 2026poster

Federated fine-tuning enables Large Language Models (LLMs) to adapt to downstream tasks while preserving data privacy, but its resource-intensive nature severely limits deployment on edge devices. In this paper, we introduce Developmental Federated Tuning (DevFT), a resource-efficient approach inspi…

Cited by 0 Source ↗Scholar ↗

2026

Don't Reinvent the Wheel, Just Realign the Spokes: Resource-Efficient Federated Fine-Tuning via Rank-Wise Expert Assembly

Yebo Wu, Jingguang Li, Zhijiang Guo, Li Li

ICML 2026spotlight

Federated fine-tuning presents a promising avenue for adapting Large Language Models (LLMs) to downstream tasks while preserving data privacy. However, the prohibitive computational and communication overhead of LLM adaptation inhibits its deployment on resource-constrained edge devices. In this pap…

Cited by 0 Source ↗Scholar ↗