20 citations · 49 across the 19 of their papers we have counts for
5 papers · 1 filter
Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning
Haibo Yang, Peiwen Qiu, Jia Liu
A key assumption in most existing works on FL algorithms' convergence analysis is that the noise in stochastic first-order information has a finite variance. Although this assumpti…
SAGDA: Achieving Communication Complexity in Federated Min-Max Learning
Haibo Yang, Zhuqing Liu, Xin Zhang +1
To lower the communication complexity of federated min-max learning, a natural approach is to utilize the idea of infrequent communications (through multiple local updates) same as…
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data
Xin Zhang, Minghong Fang, Zhuqing Liu +3
Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. A…
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…