5 papers
Fairness-Aware Federated Learning with Trajectory Shapley Value
Daniel Kuznetsov, Ziqi Wang
Federated learning is an emerging distributed paradigm that addresses the challenges posed by heterogeneous, privacy-sensitive data. It enables multiple clients to train a model co…
CRAFT: Conflict-Resolved Aggregation for Federated Training
Ziqi Wang, Qiang Liu, Nils Thuerey
The aggregation of conflicting client updates remains a fundamental bottleneck in federated learning (FL) under heterogeneous data distributions. Naive averaging can produce a glob…
Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning
Kang Liu, Ziqi Wang, Enrique Zuazua
In federated learning (FL), a central server typically allocates training efforts to clients. However, from a market-oriented perspective, clients may independently choose their tr…
Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
Xueming Yan, Ziqi Wang, Yaochu Jin
Federated multi-view clustering offers the potential to develop a global clustering model using data distributed across multiple devices. However, current methods face challenges d…
FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning
Yongcun Song, Ziqi Wang, Enrique Zuazua
Federated learning (FL) is a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challen…