Computer Science > Machine Learning
arXiv:2603.09032 (cs)
[Submitted on 10 Mar 2026]
Title:Two Teachers Better Than One: Hardware-Physics Co-Guided Distributed Scientific Machine Learning
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Abstract:Scientific machine learning (SciML) is increasingly applied to in-field processing, controlling, and monitoring; however, wide-area sensing, real-time demands, and strict energy and reliability constraints make centralized SciML implementation impractical. Most SciML models assume raw data aggregation at a central node, incurring prohibitively high communication latency and energy costs; yet, distributing models developed for general-purpose ML often breaks essential physical principles, resulting in degraded performance. To address these challenges, we introduce EPIC, a hardware- and physics-co-guided distributed SciML framework, using full-waveform inversion (FWI) as a representative task. EPIC performs lightweight local encoding on end devices and physics-aware decoding at a central node. By transmitting compact latent features rather than high-volume raw data and by using cross-attention to capture inter-receiver wavefield coupling, EPIC significantly reduces communication cost while preserving physical fidelity. Evaluated on a distributed testbed with five end devices and one central node, and across 10 datasets from OpenFWI, EPIC reduces latency by 8.9$\times$ and communication energy by 33.8$\times$, while even improving reconstruction fidelity on 8 out of 10 datasets.
| Comments: | |
| Subjects: | Machine Learning (cs.LG); Hardware Architecture (cs.AR); Computational Engineering, Finance, and Science (cs.CE); Distributed, Parallel, and Cluster Computing (cs.DC) |
| Cite as: | arXiv:2603.09032 [cs.LG] |
| (or arXiv:2603.09032v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09032
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