Title: Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction Show Chinese title

Title: 梯度压缩与相关性驱动的联邦学习在无线流量预测中的应用

Abstract: Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-based wireless traffic prediction methods still rely on heavy data transmissions between local clients and the server for local model updates. Besides, how to model the spatial dependencies of local clients under the framework of FL remains uncertain. To tackle this, we propose an innovative FL algorithm that employs gradient compression and correlation-driven techniques, effectively minimizing data transmission load while preserving prediction accuracy. Our approach begins with the introduction of gradient sparsification in wireless traffic prediction, allowing for significant data compression during model training. We then implement error feedback and gradient tracking methods to mitigate any performance degradation resulting from this compression. Moreover, we develop three tailored model aggregation strategies anchored in gradient correlation, enabling the capture of spatial dependencies across diverse clients. Experiments have been done with two real-world datasets and the results demonstrate that by capturing the spatio-temporal characteristics and correlation among local clients, the proposed algorithm outperforms the state-of-the-art algorithms and can increase the communication efficiency by up to two orders of magnitude without losing prediction accuracy. Code is available at https://github.com/chuanting/FedGCC. Show Chinese abstract

Abstract: 无线流量预测在蜂窝网络中起着不可替代的作用，以实现通信系统的主动适应。 沿着这一方向，边缘处基于联邦学习（FL）的无线流量预测引起了广泛关注，因为避免了原始数据传输并增强了隐私保护。 然而，基于FL的无线流量预测方法仍然依赖于本地客户端和服务器之间的大量数据传输来进行本地模型更新。 此外，在FL框架下如何建模本地客户端的空间依赖性仍不确定。 为了解决这个问题，我们提出了一种创新的FL算法，该算法采用梯度压缩和相关性驱动技术，有效减少数据传输负载同时保持预测准确性。 我们的方法首先引入了无线流量预测中的梯度稀疏化，允许在模型训练过程中进行显著的数据压缩。 然后，我们实现了误差反馈和梯度跟踪方法，以减轻由此压缩导致的性能下降。 此外，我们开发了三种基于梯度相关性的定制模型聚合策略，使能够捕捉不同客户端之间的空间依赖性。 我们使用两个真实世界的数据集进行了实验，结果表明，通过捕捉本地客户端之间的时空特征和相关性，所提出的算法优于最先进的算法，并且可以在不损失预测准确性的情况下将通信效率提高多达两个数量级。 代码可在 https://github.com/chuanting/FedGCC 获取。