3 accepted papers
Within distributed learning, workers typically compute gradients on their assigned dataset chunks and send them to the parameter server (PS), which aggregates them to compute either an exact or approximate version of $\nabla L$ (gradient of the loss function $L$). However, in large-scale clusters, m…
Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible to straggler (slower or failed) clients. Recent work has proposed to address this through device-to-device (D2D) offload…
Robust Subspace Tracking with missing data (RST-miss) has been extensively studied in the past decade. In this work we study RST-miss to the setting where the data is federated and when the over-air data communication modality is used for information exchange between the K peer nodes and the central…