Search Results for author:
Found 7 papers, 1 papers with code
Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage
As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount.
Decentralized Federated Unlearning on Blockchain
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.
Cryptocurrency in the Aftermath: Unveiling the Impact of the SVB Collapse
We conduct a multi-dimensional investigation, which includes a factual summary, analysis of user sentiment, and examination of market performance.
A Secure Aggregation for Federated Learning on Long-Tailed Data
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.
Distributed Trust Through the Lens of Software Architecture
Distributed trust is a nebulous concept that has evolved from different perspectives in recent years.
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey
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.
IronForge: An Open, Secure, Fair, Decentralized Federated Learning
Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner.
