Multi-Modal Collaborative Optimization of IoT Information Logistics Supply Chain Based on FL-DT Architecture

This paper studies the multi-modal collaborative optimization problem of Internet of Things (IoT) information logistics supply chain based on Federated Learning and Digital Twin (FL-DT) architecture, proposes a distributed optimization framework that integrates federated learning and decision tree, and uses multi-modal data collected by IoT technology to improve supply chain efficiency. The experiment uses real-time IoT data and order, inventory and historical logistics data for analysis. The results show that the FL-DT architecture significantly optimizes supply chain performance: order processing time is shortened by 33.33%, inventory turnover rate is increased by 26.15%, logistics cost is reduced by 20.33%, response time is compressed by about 40% and order fulfillment accuracy rate is increased from 92% to 97%. Multi-modal data fusion makes the model accuracy reach 91.2%, which is 7.8% higher than that of single-modal, the transportation efficiency is increased by 23% and the carbon emission is reduced by 14%. The digital twin simulation shows that the transportation timeliness deviation is only −0.7%, but the inventory cost deviation is [Formula: see text]2.4%, and the fitting degree is 86.4%. The dynamic prediction needs to be improved. The FL-DT architecture has small loss fluctuations in abnormal events, which is better than traditional systems. The amount of communication data is reduced by 82% to 12.7 MB/round, demonstrating high efficiency and robustness. This study provides a new method for IoT supply chain optimization, verifies its potential in efficiency improvement, cost reduction and intelligent transformation, and can be expanded to more fields in the future.