A Blockchain-based federated learning framework for secure aggregation and fair incentives
Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that in...
Saved in:
| Main Authors: | XiaoHui Yang, TianChang Li |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2024.2316018 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Blockchain-Enabled Federated Learning to Enhance Security and Privacy in Internet of Medical Things (IoMT)
by: zahra eskandari
Published: (2023-01-01) -
Design of reputation-driven blockchain sharding consensus and incentive mechanism
by: TIAN Hanwen, et al.
Published: (2025-02-01) -
BPS-FL: Blockchain-Based Privacy-Preserving and Secure Federated Learning
by: Jianping Yu, et al.
Published: (2025-02-01) -
A lightweight practical consensus mechanism for supply chain blockchain
by: Mohammad Saidur Rahman, et al.
Published: (2025-03-01) -
Edge computing privacy protection method based on blockchain and federated learning
by: Chen FANG, et al.
Published: (2021-11-01)