Papers by Jingguang Li
Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are revolutionizing mobile intelligence, but their implementation on mobile devices is severely bottlenecked by the prohibitive resource demands of LLMs. |
| Approach: | They propose a paradigm that forgoes end-to-end updates in favor of a sequential, layer-by-layer manner. |
| Outcome: | Extensive experiments on multiple benchmarks demonstrate the superiority of ChainFed over existing methods, boosting average accuracy by up to 46.46%. |