3 papers
cs.LG2026
On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach
Jiahui Bai, Hai Dong, A. K. Qin
Asynchronous decentralized federated learning (ADFL) eliminates central coordination and global synchronization, making it attractive for large-scale and heterogeneous systems. How…
cs.LG2025
On the Fast Adaptation of Delayed Clients in Decentralized Federated Learning: A Centroid-Aligned Distillation Approach
Jiahui Bai, Hai Dong, A. K. Qin
Decentralized Federated Learning (DFL) struggles with the slow adaptation of late-joining delayed clients and high communication costs in asynchronous environments. These limitatio…
cs.DC2024
FEDQ-Trust: Efficient Data-Driven Trust Prediction for Mobile Edge-Based IoT Systems
Jiahui Bai, Hai Dong, Athman Bouguettaya
We introduce FEDQ-Trust, an innovative data-driven trust prediction approach designed for mobile edge-based Internet of Things (IoT) environments. The decentralized nature of mobil…