A new framework called CIDERS has been proposed for cloud-edge collaborative learning in large language models (LLMs). This framework addresses the challenge of balancing global knowledge with local adaptation by employing a personalized bilevel optimization approach. CIDERS decomposes LLMs into a backbone and a messenger, enabling the cloud to transfer knowledge while ensuring local personalization through consensus-variate correction. Experiments show CIDERS significantly outperforms existing methods in mathematical reasoning and code generation on edge devices. AI
IMPACT Enables more efficient and personalized LLM deployment on edge devices.
RANK_REASON Research paper detailing a new framework for LLM collaboration. [lever_c_demoted from research: ic=1 ai=1.0]
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