25 citations · 100 across the 8 of their papers we have counts for
9 papers
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
Rui Ye, Mingkai Xu, Jianyu Wang +3
This work considers the category distribution heterogeneity in federated learning. This issue is due to biased labeling preferences at multiple clients and is a typical setting of…
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
John Nguyen, Jianyu Wang, Kshitiz Malik +2
An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very differe…
FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning
Rui Ye, Zhenyang Ni, Chenxin Xu +3
One of the key challenges in federated learning (FL) is local data distribution heterogeneity across clients, which may cause inconsistent feature spaces across clients. To address…
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data
Jianyu Wang, Rudrajit Das, Gauri Joshi +3
Existing theory predicts that data heterogeneity will degrade the performance of the Federated Averaging (FedAvg) algorithm in federated learning. However, in practice, the simple…
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
John Nguyen, Jianyu Wang, Kshitiz Malik +2
An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very differe…
Federated Learning under Distributed Concept Drift
Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang +2
Federated Learning (FL) under distributed concept drift is a largely unexplored area. Although concept drift is itself a well-studied phenomenon, it poses particular challenges for…
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
Jianyu Wang, Hang Qi, Ankit Singh Rawat +4
In classical federated learning, the clients contribute to the overall training by communicating local updates for the underlying model on their private data to a coordinating serv…
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer
Yae Jee Cho, Jianyu Wang, Tarun Chiruvolu +1
Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous. Most work in personalized FL…
Slow and Stale Gradients Can Win the Race
Sanghamitra Dutta, Jianyu Wang, Gauri Joshi
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in runtime as it waits for the slowest workers (stragglers). Asynchronous method…