为工业机器人设计高效隐私保护的联邦分层学习框架
Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
- 按数据处理阶段分三类融合策略，适配不同工业场景
- 实测在动态环境下仍保持高通信效率与模型精度
- 适合资源受限的工厂智能设备，兼顾隐私与算力
联邦分层学习（FedSL）已成为工业物联网中实现协作智能的有前景范式，尤其在智能制造工厂中，数据隐私、通信效率和设备异构性是关键挑战。本文针对资源受限的工业机器人，系统研究了多种FedSL框架：同步、异步、分层和异构框架，从工作流程、可扩展性、适应性及动态工业环境下的局限性进行对比分析。进一步将分片融合策略分为三类：输入级（预融合）、中间级（内融合）和输出级（后融合），总结其在工业应用中的优势。提出自适应优化技术以提升实现效率，包括模型压缩、分层选择、计算频率分配和无线资源管理。仿真结果验证了各类框架在工业检测场景下的性能表现。最后，指出未来智能制造系统中FedSL面临的关键问题与研究方向。
原文摘要 · Abstract (English)
Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart factories where data privacy, communication efficiency, and device heterogeneity are critical concerns. In this article, we present a comprehensive study of FedSL frameworks tailored for resource-constrained robots in industrial scenarios. We compare synchronous, asynchronous, hierarchical, and heterogeneous FedSL frameworks in terms of workflow, scalability, adaptability, and limitations under dynamic industrial conditions. Furthermore, we systematically categorize token fusion strategies into three paradigms: input-level (pre-fusion), intermediate-level (intra-fusion), and output-level (post-fusion), and summarize their respective strengths in industrial applications. We also provide adaptive optimization techniques to enhance the efficiency and feasibility of FedSL implementation, including model compression, split layer selection, computing frequency allocation, and wireless resource management. Simulation results validate the performance of these frameworks under industrial detection scenarios. Finally, we outline open issues and research directions of FedSL in future smart manufacturing systems.
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