14 citations · 42 across the 10 of their papers we have counts for
Showing eess.SPShow all
2 papers · 1 filter
eess.SP2023★ 14 cited
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation
Ye Xue, Vincent Lau
Low-rank model compression is a widely used technique for reducing the computational load when training machine learning models. However, existing methods often rely on relaxing th…
eess.SP2021★ 5 cited
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation
Huayan Guo, Yifan Zhu, Haoyu Ma +5
In this paper, we develop an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL). In particular, th…