用群体智能优化多模态大模型在边缘云联邦学习中的部署效率
A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments
- 结合粒子群与蚁群算法，智能选择边缘设备并优化模型传输
- 准确率达92%，通信成本降低30%，提升设备参与度
- 适合资源受限的边缘计算场景，尤其适用于大规模分布式系统
联邦学习（FL）、多模态大语言模型（MLLMs）与边缘-云计算的结合，可在保护隐私的前提下实现分布式实时数据处理。然而，在资源受限的边缘设备上部署MLLMs面临资源管理、通信开销和非独立同分布（non-IID）数据等挑战。为此，我们提出一种新型混合框架：将具备充足资源和续航能力的边缘设备用于部署MLLMs，大部分训练在云端完成，仅在边缘进行微调。通过粒子群优化（PSO）筛选合适边缘节点，利用蚁群优化（ACO）优化边缘与云节点间模型更新的传输路径。实验表明，该方法显著提升系统性能，达到92%准确率，通信成本降低30%，客户参与度更高，适用于大规模边缘-云计算系统。
原文摘要 · Abstract (English)
The combination of Federated Learning (FL), Multimodal Large Language Models (MLLMs), and edge-cloud computing enables distributed and real-time data processing while preserving privacy across edge devices and cloud infrastructure. However, the deployment of MLLMs in FL environments with resource-constrained edge devices presents significant challenges, including resource management, communication overhead, and non-IID data. To address these challenges, we propose a novel hybrid framework wherein MLLMs are deployed on edge devices equipped with sufficient resources and battery life, while the majority of training occurs in the cloud. To identify suitable edge devices for deployment, we employ Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) is utilized to optimize the transmission of model updates between edge and cloud nodes. This proposed swarm intelligence-based framework aims to enhance the efficiency of MLLM training by conducting extensive training in the cloud and fine-tuning at the edge, thereby reducing energy consumption and communication costs. Our experimental results show that the proposed method significantly improves system performance, achieving an accuracy of 92%, reducing communication cost by 30%, and enhancing client participation compared to traditional FL methods. These results make the proposed approach highly suitable for large-scale edge-cloud computing systems.
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