6 papers
Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients
Shengkun Zhu, Jinshan Zeng, Zhihua Allen-Zhao +5
Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific data cannot host billion-parame…
Topology-Aware Optimization of Gaussian Primitives for Human-Centric Volumetric Videos
Yuheng Jiang, Chengcheng Guo, Yize Wu +9
Volumetric video is emerging as a key medium for digitizing the dynamic physical world, creating the virtual environments with six degrees of freedom to deliver immersive user expe…
FedAPM: Federated Learning via ADMM with Partial Model Personalization
Shengkun Zhu, Feiteng Nie, Jinshan Zeng +6
In federated learning (FL), the assumption that datasets from different devices are independent and identically distributed (i.i.d.) often does not hold due to user differences, an…
Personalized Federated Learning via ADMM with Moreau Envelope
Shengkun Zhu, Jinshan Zeng, Sheng Wang +2
Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergence on heterogeneous data. However, most existing PFL frameworks require strong a…
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
Shengkun Zhu, Jinshan Zeng, Sheng Wang +4
Statistical heterogeneity is a root cause of tension among accuracy, fairness, and robustness of federated learning (FL), and is key in paving a path forward. Personalized FL (PFL)…
Efficient k-means with Individual Fairness via Exponential Tilting
Shengkun Zhu, Jinshan Zeng, Yuan Sun +3
In location-based resource allocation scenarios, the distances between each individual and the facility are desired to be approximately equal, thereby ensuring fairness. Individual…