用硬件与物理规律协同指导，让分布式科学机器学习更高效可靠
Two Teachers Better Than One: Hardware-Physics Co-Guided Distributed Scientific Machine Learning
- 端侧轻量编码+中心物理解码，结合跨注意力捕捉波场耦合
- 通信延迟降8.9倍，能耗降33.8倍，8个数据集重建精度提升
- 适合边缘计算、地震反演等需保物理一致性的实时科学任务
科学机器学习（SciML）在野外处理、控制和监测中日益重要；然而广域传感、实时性要求及严格的能量与可靠性约束使集中式实现不可行。多数SciML模型假设原始数据汇聚至中心节点，导致通信延迟和能耗过高；而直接将通用机器学习模型分布化常破坏基本物理规律，导致性能下降。为此，我们提出EPIC框架，以全波形反演（FWI）为例，通过端设备轻量编码与中心节点物理感知解码，在传输紧凑隐特征而非高维原始数据的同时，利用交叉注意力建模多接收器间波场耦合，显著降低通信开销并保持物理一致性。在包含五个终端设备与一个中心节点的分布式测试平台，覆盖OpenFWI中的10个数据集评估表明，EPIC将延迟降低8.9倍、通信能耗降低33.8倍，且在8个数据集上重建精度反而提升。
原文摘要 · Abstract (English)
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.
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