Edge computing is a distributed computing paradigm that collects and processes data at or near the source of data generation. The on-device learning at edge relies on device-to-device wireless communication to facilitate real-time data sharing and collaborative decision-making among multiple devices. This significantly improves the adaptability of the edge computing system to the changing environments. However, as the scale of the edge computing system is getting larger, communication among devices is becoming the bottleneck because of the limited bandwidth of wireless communication leads to large data transfer latency. To reduce the amount of device-to-device data transmission and accelerate on-device learning, in this paper, we propose Residual-INR, a fog computing-based communication-efficient on-device learning framework by utilizing implicit neural representation (INR) to compress images/videos into neural network weights. Residual-INR enhances data transfer efficiency by collecting JPEG images from edge devices, compressing them into INR format at the fog node, and redistributing them for on-device learning. By using a smaller INR for full image encoding and a separate object INR for high-quality object region reconstruction through residual encoding, our technique can reduce the encoding redundancy while maintaining the object quality. Residual-INR is a promising solution for edge on-device learning because it reduces data transmission by up to 5.16 x across a network of 10 edge devices. It also facilitates CPU-free accelerated on-device learning, achieving up to 2.9 x speedup without sacrificing accuracy. Our code is available at: https://github.com/sharc-lab/Residual-INR.

翻译：边缘计算是一种分布式计算范式，其在数据生成源头或附近进行数据收集与处理。边缘设备端学习依赖于设备间无线通信，以实现多设备间的实时数据共享与协同决策，从而显著提升边缘计算系统对动态环境的适应能力。然而，随着边缘计算系统规模不断扩大，有限的无线通信带宽导致数据传输延迟增加，设备间通信正逐渐成为系统瓶颈。为减少设备间数据传输量并加速设备端学习，本文提出Residual-INR——一种基于雾计算的通信高效设备端学习框架，该框架利用隐式神经表征（INR）将图像/视频压缩为神经网络权重。Residual-INR通过从边缘设备收集JPEG图像，在雾节点将其压缩为INR格式并重新分发至设备端进行学习，从而提升数据传输效率。本技术采用小型INR进行全图编码，同时通过残差编码使用独立的对象INR实现高质量对象区域重建，在保持对象质量的同时减少了编码冗余。Residual-INR在10台边缘设备组成的网络中可实现最高5.16倍的数据传输压缩，并能实现无需CPU加速的设备端学习，在保持精度前提下获得最高2.9倍的加速比，是极具前景的边缘设备端学习解决方案。代码已开源：https://github.com/sharc-lab/Residual-INR。