5 papers
FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data
Yue Chen, Jianfeng Lu, Shuqing Cao +3
While semi-asynchronous federated learning (SAFL) combines the efficiency of synchronous training with the flexibility of asynchronous updates, it inherently suffers from participa…
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information
Yun Xin, Jianfeng Lu, Shuqin Cao +3
Online Federated Learning (OFL) is a real-time learning paradigm that sequentially executes parameter aggregation immediately for each random arriving client. To motivate clients t…
FedCross: Intertemporal Federated Learning Under Evolutionary Games
Jianfeng Lu, Ying Zhang, Riheng Jia +3
Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks…
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
Gangqiang Hu, Jianfeng Lu, Jianmin Han +3
Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of var…
LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game
Jianfeng Lu, Yue Chen, Shuqin Cao +3
Although Hierarchical Federated Learning (HFL) utilizes edge servers (ESs) to alleviate communication burdens, its model performance will be degraded by non-IID data and limited co…