Privacy-Enhanced Federated Learning Against Poisoning Adversaries

By Hongwei Li, Guowen Xu, Zongqi Chen, Xiaoming Huang, Rongxing Lu. IEEE Transactions on Information Forensics and Security, 2021

314 citations recorded by the source corpus.

Federated learning (FL), as a distributed machine learning setting, has received considerable attention in recent years. To alleviate privacy concerns, FL essentially promises that multiple parties jointly train the model by exchanging gradients rather than raw data. However, intrinsic privacy issue still exists in FL, e.g., user’s training samples could be revealed by solely inferring gradients. Moreover, the emerging poisoning attack also poses a crucial security threat to FL. In particular, due to the distributed nature of FL, malicious users may submit crafted gradients during the training process to undermine the integrity and availability of the model. Furthermore, there exists a contradiction in simultaneously addressing two issues, that is, privacy-preserving FL solutions are dedicated to ensuring gradients indistinguishability, whereas the defenses against poisoning attacks tend to remove outliers based on their similarity. To solve such a dilemma, in this paper, we aim to build a bridge between the two issues. Specifically, we present a privacy-enhanced FL (PEFL) framework that adopts homomorphic encryption as the underlying technology and provides the server with a channel to punish poisoners via the effective gradient data extraction of the logarithmic function. To the best of our knowledge, the PEFL is the first effort to efficiently detect the poisoning behaviors in FL under ciphertext. Detailed theoretical analyses illustrate the security and convergence properties of the scheme. Moreover, the experiments conducted on real-world datasets show that the PEFL can effectively defend against label-flipping and backdoor attacks, two representative poisoning attacks in FL.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/5b71f0d522f7a5106e3227b6b814f0e94618f672

How researchers see this paper

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In Artificial Intelligence

1. Zhifeng Li
2. Yong Jiang
3. Baoyuan Wu
4. Ali Dehghantanha
5. Shutao Xia
6. Yiming Li

Citations in the corpus

• A Robust Privacy-Preserving Federated Learning Model Against Model Poisoning Attacks (2024, methodology): “Model poisoning adversaries manipulate user input by submitting poisonous updates to local models to create arbitrary deviance from correct trends [3].”
• Backdoor Learning: A Survey (2020, result): “In contrast, some works [30], [101]–[103] also questioned whether federal learning is really easy to be attacked.”