1 code implementation • 9 Apr 2024 • Yilin Sai, Qin Wang, Guangsheng Yu, H. M. N. Dilum Bandara, Shiping Chen
As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount.
no code implementations • 26 Feb 2024 • Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu, Wei Ni, Lixiang Li, Haipeng Peng, Renping Liu
To address this, we propose Blockchained Federated Unlearning (BlockFUL), a generic framework that redesigns the blockchain structure using Chameleon Hash (CH) technology to mitigate the complexity of model updating, thereby reducing the computational and consensus costs of unlearning tasks. Furthermore, BlockFUL supports various federated unlearning methods, ensuring the integrity and traceability of model updates, whether conducted in parallel or serial.
no code implementations • 15 Sep 2023 • Qin Wang, Guangsheng Yu, Shiping Chen
We conduct a multi-dimensional investigation, which includes a factual summary, analysis of user sentiment, and examination of market performance.
no code implementations • 17 Jul 2023 • Yanna Jiang, Baihe Ma, Xu Wang, Guangsheng Yu, Caijun Sun, Wei Ni, Ren Ping Liu
As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes.
no code implementations • 25 May 2023 • Sin Kit Lo, Yue Liu, Guangsheng Yu, Qinghua Lu, Xiwei Xu, Liming Zhu
Distributed trust is a nebulous concept that has evolved from different perspectives in recent years.
no code implementations • 8 May 2023 • Yanna Jiang, Baihe Ma, Xu Wang, Ping Yu, Guangsheng Yu, Zhe Wang, Wei Ni, Ren Ping Liu
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world.
no code implementations • 7 Jan 2023 • Guangsheng Yu, Xu Wang, Caijun Sun, Qin Wang, Ping Yu, Wei Ni, Ren Ping Liu, Xiwei Xu
Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner.