Summary of Fedcert: Federated Accuracy Certification, by Minh Hieu Nguyen et al.
FedCert: Federated Accuracy Certification
by Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen, Manh Duong Nguyen, Trong Nghia Hoang, Truong Thao Nguyen, Phi Le Nguyen
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FedCert method approximates the certified accuracy of a global model in Federated Learning (FL) systems by considering the certified accuracy and class distribution of each client. This approach tackles the challenge of evaluating robustness against data perturbations on clients, which is crucial for preserving data privacy. The method involves client grouping to ensure reliable certified accuracy during the aggregation step. Experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that FedCert consistently reduces estimation error compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning helps train machine learning models without sharing private data. But, it’s hard to test how well these models work when the data is changed. This paper proposes a new way to evaluate how well FL models do under different conditions. They call this method FedCert and use it to check how well global models perform on local data. The results show that FedCert works better than other methods at estimating how accurate the models are. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
