collaborative-inference

Lluna improves multimodal predictions when some input modalities are missing by combining private unimodal predictions with a graph-based residual collaboration branch and progressive consistency training, achieving a 4.26% average improvement in Mean-Missing over baselines across eight datasets.

This paper introduces X-CoSD, a communication-efficient cross-vocabulary collaborative speculative decoding framework that optimizes distributed LLM inference by splitting residual resampling to reduce overhead while preserving server LLM quality.

本文研究垂直联邦学习中的选择性升级问题，提出一种基于期望增益的评分方法，在低成本的本地预测和高成本的嵌入融合之间进行路由，以优化通信-准确率权衡。