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FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
[article]
2022
arXiv
pre-print
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In this work, we bridge the gap via proposing FLTrust, a new federated learning method in which the service provider itself bootstraps trust. ...
Byzantine-robust federated learning aims to enable a service provider to learn an accurate global model when a bounded number of clients are malicious. ...
In this work, we bridge the gap via proposing FLTrust, a new federated learning method in which the service provider itself bootstraps trust. ...
arXiv:2012.13995v3
fatcat:7xgtvkwsgrhh3fualpykw7re54
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
2021
Proceedings 2021 Network and Distributed System Security Symposium
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this work, we bridge the gap via proposing FLTrust, a new federated learning method in which the service provider itself bootstraps trust. ...
Byzantine-robust federated learning aims to enable a service provider to learn an accurate global model when a bounded number of clients are malicious. ...
In this work, we bridge the gap via proposing FLTrust, a new federated learning method in which the service provider itself bootstraps trust. ...
doi:10.14722/ndss.2021.24434
fatcat:h5n3ivcwujalrc4bde6z2z4bs4
Byzantine-Resilient Federated Learning with Heterogeneous Data Distribution
[article]
2022
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
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2021 pre-print arXiv:2010.07541v3 fatcat:osjh7tyiovf2ngsxlmdxtwhnnmOther Versions
For mitigating Byzantine behaviors in federated learning (FL), most state-of-the-art approaches, such as Bulyan, tend to leverage the similarity of updates from the benign clients. ...
However, in federated learning (FL), data distribution across clients is typically heterogeneous. ...
Client nodes that provide arbitrary updates are called Byzantine clients (Lamport et al., 2019) , and it is imperative to make federated learning robust to these behaviors. ...
arXiv:2010.07541v4
fatcat:w6ujccz5kjdxresmixh6wj2iii
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning
[article]
2021
arXiv
pre-print
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In this paper, we propose a novel Robust and Fair Federated Learning (RFFL) framework to achieve collaborative fairness and adversarial robustness simultaneously via a reputation mechanism. ...
Federated learning (FL) is an emerging practical framework for effective and scalable machine learning among multiple participants, such as end users, organizations and companies. ...
FLTrust: Byzantine-robust federated learning via trust bootstrapping. In Proceedings of the Network and Distributed Systems Security, 2020. Fung, C., Yoon, C. J. M., and Beschastnikh, I. ...
arXiv:2011.10464v2
fatcat:xyej4d7sgrc53jeq22cfib2bk4