7 papers
Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penalty
Ziruo Hao, Zhenhua Cui, Tao Yang +3
This paper provides an invariant federated learning system for resource-constrained edge intelligence. This framework can mitigate the impact of heterogeneity and asynchrony via ex…
MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments
Zhiyin Li, Yubo Yang, Tao Yang +3
Federated learning enables distributed model training across clients without raw data exchange, but in wireless implementations, frequent parameter updates cause high communication…
Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inference
Songlin Yang, Tao Yang, Bo Hu
The deployment of sensors for air quality monitoring is constrained by high costs, leading to inadequate network coverage and data deficits in some areas. Utilizing existing observ…
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing
Yubo Yang, Tao Yang, Xiaofeng Wu +2
UAV swarms are widely used in emergency communications, area monitoring, and disaster relief. Coordinated by control centers, they are ideal for federated learning (FL) frameworks.…
Drift-Aware Federated Learning: A Causal Perspective
Yunjie Fang, Sheng Wu, Tao Yang +2
Federated learning (FL) facilitates collaborative model training among multiple clients while preserving data privacy, often resulting in enhanced performance compared to models tr…
The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence
Ziruo Hao, Zhenhua Cui, Tao Yang +3
Federated learning (FL) has provided a new methodology for coordinating a group of clients to train a machine learning model collaboratively, bringing an efficient paradigm in edge…