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Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction

2026-06-23 · Wenjie Ding, Yi Sin Lin, Jiale Liu, Baoyi Liu, Guanghua Liu, Zhuolu Li, Suleiman Sabo, Chuadhry Mujeeb Ahmed, Aydin Abadi, Rehmat Ullah, Rajiv Ranjan arxiv

Federated split learning (FSL) enables collaborative training across bandwidth-constrained IoT devices, but repeated activation and gradient exchange creates a communication bot-tleneck. Prior work optimises either activation compression or synchronisation frequency in isolation. This paper presents an FSL framework for IoT rainfall prediction that jointly regulates activation compression and the synchronisation interval \r{ho} via a latency driven scheduler on a server with per client EMA smoothing. The system is evaluated on hourly ERA5 data from 11 weather stations through a 17 scenario simulation matrix and a four scenario Raspberry Pi deployment over a real wide-area link. The simulation matrix validates scheduler switching across low, high, and mixed latency profiles, while the Pi deployment validates the high latency endpoint selected by the same policy. AUPRC varies only slightly across configurations (0.6381-0.6484 in simulation; within 0.011 on Pi), indicating that aggressive quantisation and sparser aggregation do not materially degrade predictive quality in this setting. On Pi, the selected endpoint (int8 with rho=3) achieves an 87% reduction in activation upload payload and a 54% reduction in synchronisation traffic relative to the float32 baseline, while reducing runtime jitter from +/-688 s to +/-10 s.

📄 PDF Abstract BibTeX arXiv:2606.25003

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Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency

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2026-02-19 · Chuiyang Meng, Ming Tang, Vincent W. S. Wong arxiv

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Synchronisation-Oriented Design Approach for Adaptive Control

2024-03-14 · Namhoon Cho, Seokwon Lee, Hyo-Sang Shin

This study presents a synchronisation-oriented perspective towards adaptive control which views model-referenced adaptation as synchronisation between actual and virtual dynamic systems. In the context of adaptation, mod…

SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression

2025-08-18 · Zehang Lin, Zheng Lin, Miao Yang, Jianhao Huang 외 arxiv

The increasing complexity of neural networks poses a significant barrier to the deployment of distributed machine learning (ML) on resource-constrained devices, such as federated learning (FL). Split learning (SL) offers…