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Federated edge learning (FEEL) is a promising distributed machine learning(ML) framework to drive edge intelligence applications. However, due to thedynamic wireless environments and the resource limitations of edge devices,communication becomes a major bottleneck. In this work, we proposetime-correlated sparsification with hybrid aggregation (TCS-H) forcommunication-efficient FEEL, which exploits jointly the power of modelcompression and over-the-air computation. By exploiting the temporalcorrelations among model parameters, we construct a global sparsification mask,which is identical across devices, and thus enables efficient model aggregationover-the-air. Each device further constructs a local sparse vector to exploreits own important parameters, which are aggregated via digital communicationwith orthogonal multiple access. We further design device scheduling and powerallocation algorithms for TCS-H. Experiment results show that, under limitedcommunication resources, TCS-H can achieve significantly higher accuracycompared to the conventional top-K sparsification with orthogonal modelaggregation, with both i.i.d. and non-i.i.d. data distributions.

Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning