314 citations · 341 across the 25 of their papers we have counts for
7 papers · 1 filter
Why Go Full? Elevating Federated Learning Through Partial Network Updates
Haolin Wang, Xuefeng Liu, Jianwei Niu +2
Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditiona…
GLC-SLAM: Gaussian Splatting SLAM with Efficient Loop Closure
Ziheng Xu, Qingfeng Li, Chen Chen +2
3D Gaussian Splatting (3DGS) has gained significant attention for its application in dense Simultaneous Localization and Mapping (SLAM), enabling real-time rendering and high-fidel…
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
Xinghao Wu, Jianwei Niu, Xuefeng Liu +4
Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model's performance across diverse client distributions. Personalized Federated Learning…
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
Xinghao Wu, Xuefeng Liu, Jianwei Niu +4
Personalized Federated Learning (PFL) is a commonly used framework that allows clients to collaboratively train their personalized models. PFL is particularly useful for handling s…
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
Guogang Zhu, Xuefeng Liu, Jianwei Niu +3
In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to t…
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
Xinghao Wu, Xuefeng Liu, Jianwei Niu +4
To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL) is to decouple general knowledge (shared among clients) and client-specific knowledge, as t…
Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning
Guogang Zhu, Xuefeng Liu, Xinghao Wu +4
Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model.In FSSL, the heterogeneous data can introduce predic…