Edge signal processing facilitates distributed learning and inference in the client-server model proposed in federated learning. In traditional machine learning, clients (IoT devices) that acquire raw signal samples can aid a data center (server) learn a global signal model by pooling these distributed samples at a third-party location. Despite the promising capabilities of IoTs, these distributed deployments often face the challenge of sensitive private data and communication rate constraints. This necessitates a learning approach that communicates a processed approximation of the distributed samples instead of the raw signals. Such a decentralized learning approach using signal approximations will be termed distributed signal analytics in this work. Overpredictive signal approximations may be desired for distributed signal analytics, especially in network demand (capacity) planning applications motivated by federated learning. In this work, we propose algorithms that compute an overpredictive signal approximation at the client devices using an efficient convex optimization framework. Tradeoffs between communication cost, sampling rate, and the signal approximation error are quantified using mathematical analysis. We also show the performance of the proposed distributed algorithms on a publicly available residential energy consumption dataset.

翻译：边缘信号处理促进了联邦学习所提出的客户端-服务器模型中的分布式学习与推断。在传统机器学习中，采集原始信号样本的客户端（物联网设备）可通过在第三方位置汇集这些分布式样本，协助数据中心（服务器）学习全局信号模型。尽管物联网技术前景广阔，这些分布式部署常面临敏感私有数据与通信速率限制的挑战。这需要一种学习范式，其通信内容为分布式样本的经处理近似值而非原始信号。此类使用信号近似的去中心化学习方法在本文中将被定义为分布式信号分析。对于分布式信号分析，尤其是在联邦学习驱动的网络需求（容量）规划应用中，过度预测的信号近似可能是必要的。本文提出一种基于高效凸优化框架的算法，可在客户端设备计算过度预测信号近似。通过数学分析量化了通信成本、采样率与信号近似误差之间的权衡关系。我们还在公开可用的居民能耗数据集上展示了所提分布式算法的性能表现。