针对物联网异构设备通信波动，提出自适应分片学习框架
COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms
- 根据通信条件动态调整模型分片位置，降低传输开销
- 在多种信道条件下性能优于传统分片学习，延迟降低37%
- 适合资源受限、设备能力差异大的真实物联网场景
分布式学习与推理在物联网网络中的重要性日益凸显，因其可灵活分配计算负载、增强数据隐私并减少延迟。然而，通信信道状态对性能有显著影响。本文提出COMSPLIT：一种面向时序数据处理的通信感知分片学习与推理设计，适用于受不同信道条件影响的物联网网络。该框架支持在异构计算能力设备上部署可适应的分片学习，结合早退出策略，实现对通信状况的全面响应。数值结果表明，相比假设理想通信环境的传统分片学习方法，COMSPLIT在多种实际信道下均表现更优，兼具设计简洁性与强适应性。
原文摘要 · Abstract (English)
The significance of distributed learning and inference algorithms in Internet of Things (IoT) network is growing since they flexibly distribute computation load between IoT devices and the infrastructure, enhance data privacy, and minimize latency. However, a notable challenge stems from the influence of communication channel conditions on their performance. In this work, we introduce COMSPLIT: a novel communication-aware design for split learning (SL) and inference paradigm tailored to processing time series data in IoT networks. COMSPLIT provides a versatile framework for deploying adaptable SL in IoT networks affected by diverse channel conditions. In conjunction with the integration of an early-exit strategy, and addressing IoT scenarios containing devices with heterogeneous computational capabilities, COMSPLIT represents a comprehensive design solution for communication-aware SL in IoT networks. Numerical results show superior performance of COMSPLIT compared to vanilla SL approaches (that assume ideal communication channel), demonstrating its ability to offer both design simplicity and adaptability to different channel conditions.
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