20 citations · 22 across the 4 of their papers we have counts for
5 papers
CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks
Jiayu Mao, Haibo Yang, Peiwen Qiu +2
Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that exploits the superposition property of the wireless medium and performs model aggregation for fe…
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
Haibo Yang, Peiwen Qiu, Jia Liu +1
This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the superposition property of the wireless medium, and performs model aggregation over the air for fr…
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Prashant Khanduri, Pranay Sharma, Haibo Yang +4
Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. Despite extensive research, for a generic non-convex FL pr…
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning
Haibo Yang, Jia Liu, Elizabeth S. Bentley
Federated learning (FL) is a prevailing distributed learning paradigm, where a large number of workers jointly learn a model without sharing their training data. However, high comm…
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Haibo Yang, Minghong Fang, Jia Liu
Federated learning (FL) is a distributed machine learning architecture that leverages a large number of workers to jointly learn a model with decentralized data. FL has received in…