Transparent Contribution Evaluation for Secure Federated Learning ...
Fairness and privacy preserving in federated learning: A survey
... contribution evaluation results. Unfairness in the evaluation of client contributions can have far-reaching effects on the entire FL training process. (iv) ...
曹 洋 (Yang Cao) - Transparent Contribution Evaluation for Secure ...
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain. 37th IEEE International Conference on Data Engineering Workshops. Shuaicheng ...
ACE: A Model Poisoning Attack on Contribution Evaluation Methods ...
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning ... However, the security of contribution evaluation methods of FL ...
A Blockchain-based federated learning framework for secure ...
A non-cooperative game form is used to evaluate the contributions of different roles. An incentive mechanism based on contribution and reputation is proposed.
Trustworthy Federated Learning - IEEE Xplore
more secure, transparent, and reliable decentralized learning ... Efficient and accurate participant contribution evaluation in federated.
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain · Ma, SC; Cao, Y; Xiong, L · 2021.
Shuaicheng Ma - Google Scholar
Transparent contribution evaluation for secure federated learning on blockchain. S Ma, Y Cao, L Xiong. 2021 IEEE 37th international conference on data ...
ICASSP-2023-24-Papers/sections/2024/main/SPCOM.md ... - GitHub
Towards Resource-Efficient and Secure Federated ... AdaFL: Adaptive Client Selection and Dynamic Contribution Evaluation for Efficient Federated Learning ...
A Secure and Fair Federated Learning Framework Based on ... - MDPI
Furthermore, we have proposed a fair contribution assessment method and awarded the right to write blocks to the creator of the optimal model, ensuring the ...
A Survey of Trustworthy Federated Learning: Issues, Solutions, and ...
Privacy and Security are the key factors that contribute ... GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning.
... training. Fairness · Paper · Add Code · Transparent Contribution Evaluation for Secure Federated Learning on Blockchain · no code implementations • 26 Jan 2021 ...
What is Federated Learning? - Flower Framework
Federated learning, federated evaluation, and federated analytics require infrastructure to move machine learning ... secure way. In short, Flower presents ...
Transparent contribution evaluation for secure federated learning on blockchain. S Ma, Y Cao, L Xiong. 2021 IEEE 37th international conference on data ...
An Overview of Data Contribution Evaluation Methods for Federated ...
Abstract: With the rapid development of artificial intelligence, especially machine learning technology, the demand for data.
Data valuation for machine learning and federated learning
Xiong, 'Transparent contribution evaluation for secure federated learn- ing on blockchain,' arXiv preprint arXiv:2101.10572, 2021. [30] G. Wang, C. X. Dang ...
Proof-of-authority-based secure and efficient aggregation with ...
Li, Blockchain for federated learning toward secure distributed machine learning ... Ma, Transparent contribution evaluation for secure federated learning on ...
Measuring Contributions in Privacy-Preserving Federated Learning
TRAPEZE – Transparency, Privacy and Security for European Citizens ... contribution score evaluation on which all participants agree. Our ...
A Survey on Contribution Evaluation in Vertical Federated Learning
... contributions to the model. Sustainable Collaboration: A fair and transparent contribution evaluation process helps maintain a healthy, long ...
Federated Learning and Explainable AI: Enhancing Privacy ...
This allows for improved data security, reduces the need for massive data transfers, and enhances the scalability of machine learning solutions.
Transparent and Accountable Training Data Sharing in ...
This transparency in contribution evaluation ... Qadir, “Incentive-driven federated learning and associated security challenges: A systematic review,” 2021.