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：高效压缩相关数据对于减少多传感器网络中的通信负载至关重要。在此类网络中，由于通信带宽有限，每个传感器独立压缩数据并将其传输至中央节点。中央节点的解码器解压缩数据后，将其传递给基于预训练机器学习任务以生成最终输出。因此，压缩与任务相关的特征具有重要意义。此外，最终性能严重依赖于总可用带宽。实践中常遇到带宽可用性动态变化的情况，且更高带宽能带来更好的任务性能。我们设计了一种由独立编码器和联合解码器组成的新型分布式压缩框架，称为神经分布式主成分分析（NDPCA）。NDPCA通过单一模型灵活地将多源数据压缩至任意可用带宽，降低了计算与存储开销。该框架通过学习低秩任务表示并在传感器间高效分配带宽，实现了性能与带宽之间的平滑权衡。实验表明，与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中将成功率提升9%，在卫星图像目标检测任务中将准确率提升14%。