Cooperative Gradient Coding for Collaborative Federated Learning

We investigate federated learning (FL) in the presence of stragglers, with emphasis on wireless scenarios where the power-constrained edge devices collaboratively train a global model on their local datasets and transmit local model updates through fading channels. To tackle stragglers resulting from link disruptions without requiring accurate prior information on connectivity or dataset sharing, we propose a gradient coding (GC) scheme based on cooperative communication, which remains valid for general collaborative federated learning. Furthermore, we conduct an outage analysis of the proposed scheme, based on which we conduct the convergence analysis. The simulation results reveal the superiority of the proposed strategy in the presence of stragglers, especially under imbalanced data distribution.

Wideband mmWave Massive MIMO Channel Estimation and Localization

Spatial wideband effects are known to affect channel estimation and localization performance in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Based on perturbation analysis, we show that the spatial wideband effect is in fact more pronounced than previously thought and significantly degrades performance, even at moderate bandwidths, if it is not properly considered in the algorithm design. We propose a novel channel estimation method based on multidimensional ESPRIT per subcarrier, combined with unsupervised learning for pairing across subcarriers, which shows significant performance gain over existing schemes under wideband conditions.