2 citations · 2 across the 7 of their papers we have counts for
7 papers
FWeb3: A Practical Incentive-Aware Federated Learning Framework
Peishen Yan, Shuang Liang, Yang Hua +9
Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate con…
SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)
Shuang Liang, Yang Hua, Linshan Jiang +4
In open Federated Learning (FL) environments where no central authority exists, ensuring collaboration fairness relies on decentralized reward settlement, yet the prohibitive cost…
NF-SecRIS: RIS-Assisted Near-Field Physical Layer Security via Secure Location Modulation
Zhendong Wang, Chenyang Meng, Jun Yang +4
The 6G wireless networks impose extremely high requirements on physical layer secure communication. However, the existing solutions usually can only achieve one-dimensional physica…
POLAR: Policy-based Layerwise Reinforcement Learning Method for Stealthy Backdoor Attacks in Federated Learning
Kuai Yu, Xiaoyu Wu, Peishen Yan +6
Federated Learning (FL) enables decentralized model training across multiple clients without exposing local data, but its distributed feature makes it vulnerable to backdoor attack…
"They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensing
Moming Duan, Rui Zhao, Linshan Jiang +2
As model parameter sizes scale into the billions and training consumes zettaFLOPs of computation, the reuse of Machine Learning (ML) assets and collaborative development have becom…
OFL-W3: A One-shot Federated Learning System on Web 3.0
Linshan Jiang, Moming Duan, Bingsheng He +4
Federated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables the…
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives
Moming Duan, Qinbin Li, Linshan Jiang +1
Traditional Federated Learning (FL) follows a server-dominated cooperation paradigm which narrows the application scenarios of FL and decreases the enthusiasm of data holders to pa…