arXiv Machine Learning By Wenjie Ding, Yi Sin Lin, Jiale Liu, Baoyi Liu, Guanghua Liu, Zhuolu Li, Suleiman Sabo, Chuadhry Mujeeb Ahmed, Aydin Abadi, Rehmat Ullah, Rajiv Ranjan

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

Read the original on arXiv Machine Learning →

arXiv:2606. 25003v1 Announce Type: new Abstract: Federated split learning (FSL) enables collaborative training across bandwidth-constrained IoT devices, but repeated activation and gradient exchange creates a communication bot-tleneck.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
1d ago

A Low-Cost IoT Device for Environmental Monitoring and Embedded Solar Forecasting with On-Device Incremental Learning

arXiv:2608. 14698v1 Announce Type: cross Abstract: Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require investments easily exceeding 1000~USD per node, making distributed deployments economically inaccessible.

By Erick Michel Lara Pinal, Abhinav Das, Stephan Schl\"uter
arXiv AI
Jun 3

AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling

arXiv:2606. 03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control.

By Maxime Elkael, Michele Polese, Yunseong Lee, Koichiro Furueda, Tommaso Melodia