20 citations · 49 across the 19 of their papers we have counts for
4 papers · 1 filter
Anarchic Federated Learning
Haibo Yang, Xin Zhang, Prashant Khanduri +1
Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabiliti…
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…