Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions

2025-10-07

AI Summary

To address data privacy preservation, high communication overhead, and device heterogeneity in resource-constrained Industrial IoT (IIoT) robotic systems, this work proposes a co-optimized Federated Split Learning (FSL) framework. Methodologically, we design four system architectures—synchronous, asynchronous, hierarchical, and heterogeneous—and systematically categorize token fusion strategies into input-level, intermediate-level, and output-level variants. We further integrate adaptive layer splitting, model compression, and joint computation-communication scheduling. Experiments on industrial defect detection demonstrate that our approach reduces communication volume by 42% and improves model accuracy by 8.3% over baseline methods, while significantly enhancing scalability and robustness under dynamic operational conditions. This work establishes a deployable technical pathway for lightweight, privacy-preserving collaborative intelligence in smart factories.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Optimizing federated split learning for resource-constrained robots
• Comparing frameworks for scalability and adaptability in IoT
• Enhancing efficiency with model compression and resource management

Innovation

• Federated split learning for resource-constrained robots
• Token fusion strategies at three different levels
• Adaptive optimization techniques for efficient implementation