Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data and transmits them to a central node due to limited communication bandwidth. A decoder at the central node decompresses and passes the data to a pre-trained machine learning-based task to generate the final output. Thus, it is important to compress the features that are relevant to the task. Additionally, the final performance depends heavily on the total available bandwidth. In practice, it is common to encounter varying availability in bandwidth, and higher bandwidth results in better performance of the task. We design a novel distributed compression framework composed of independent encoders and a joint decoder, which we call neural distributed principal component analysis (NDPCA). NDPCA flexibly compresses data from multiple sources to any available bandwidth with a single model, reducing computing and storage overhead. NDPCA achieves this by learning low-rank task representations and efficiently distributing bandwidth among sensors, thus providing a graceful trade-off between performance and bandwidth. Experiments show that NDPCA improves the success rate of multi-view robotic arm manipulation by 9% and the accuracy of object detection tasks on satellite imagery by 14% compared to an autoencoder with uniform bandwidth allocation.

翻译：相关数据的高效压缩对于减少多传感器网络中的通信开销至关重要。在此类网络中，由于通信带宽有限，各传感器独立压缩数据并将其传输至中心节点。中心节点处的解码器解压数据后，将其传递给基于预训练机器学习模型的任务以生成最终输出。因此，压缩与任务相关的特征至关重要。此外，最终性能严重依赖于总可用带宽。实际应用中，带宽的波动十分常见，且更高的带宽能带来更优的任务性能。我们设计了一种新颖的分布式压缩框架，由独立编码器和联合解码器构成，称之为神经分布式主成分分析（NDPCA）。NDPCA能够通过单一模型灵活地将多源数据压缩至任意可用带宽，从而降低计算与存储开销。其核心在于学习低秩任务表征并在传感器间高效分配带宽，进而实现性能与带宽之间的优雅权衡。实验表明，与采用均匀带宽分配的自动编码器相比，NDPCA在多视角机械臂操作任务上的成功率提升了9%，在卫星图像目标检测任务上的准确率提升了14%。