Peiwen Qiu — accepted AI-conference papers

2024

Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation

Haibo Yang, Peiwen Qiu, Prashant Khanduri, Minghong Fang, Jia Liu

ICML 2024poster

Existing works in federated learning (FL) often assume either full client or uniformly distributed client participation. However, in reality, some clients may never participate in FL training (aka incomplete client participation) due to various system heterogeneity factors. A popular solution is the…

Cited by 1 Source ↗PDF ↗Scholar ↗

2022

Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning

Haibo Yang, Peiwen Qiu, Jia Liu

NeurIPS 2022accept

In recent years, federated learning (FL) has emerged as an important distributed machine learning paradigm to collaboratively learn a global model with multiple clients, while keeping data local and private. However, a key assumption in most existing works on FL algorithms' convergence analysis is t…

Cited by 13 Source ↗PDF ↗Scholar ↗