Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914944762511360 |
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| author | Zhang, Yujie Gong, Neil Reiter, Michael K. |
| author_facet | Zhang, Yujie Gong, Neil Reiter, Michael K. |
| contents | Federated Learning (FL) is a decentralized machine learning method that enables participants to collaboratively train a model without sharing their private data. Despite its privacy and scalability benefits, FL is susceptible to backdoor attacks, where adversaries poison the local training data of a subset of clients using a backdoor trigger, aiming to make the aggregated model produce malicious results when the same backdoor condition is met by an inference-time input. Existing backdoor attacks in FL suffer from common deficiencies: fixed trigger patterns and reliance on the assistance of model poisoning. State-of-the-art defenses based on analyzing clients' model updates exhibit a good defense performance on these attacks because of the significant divergence between malicious and benign client model updates. To effectively conceal malicious model updates among benign ones, we propose DPOT, a backdoor attack strategy in FL that dynamically constructs backdoor objectives by optimizing a backdoor trigger, making backdoor data have minimal effect on model updates. We provide theoretical justifications for DPOT's attacking principle and display experimental results showing that DPOT, via only a data-poisoning attack, effectively undermines state-of-the-art defenses and outperforms existing backdoor attack techniques on various datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06206 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning Zhang, Yujie Gong, Neil Reiter, Michael K. Cryptography and Security Artificial Intelligence Machine Learning Federated Learning (FL) is a decentralized machine learning method that enables participants to collaboratively train a model without sharing their private data. Despite its privacy and scalability benefits, FL is susceptible to backdoor attacks, where adversaries poison the local training data of a subset of clients using a backdoor trigger, aiming to make the aggregated model produce malicious results when the same backdoor condition is met by an inference-time input. Existing backdoor attacks in FL suffer from common deficiencies: fixed trigger patterns and reliance on the assistance of model poisoning. State-of-the-art defenses based on analyzing clients' model updates exhibit a good defense performance on these attacks because of the significant divergence between malicious and benign client model updates. To effectively conceal malicious model updates among benign ones, we propose DPOT, a backdoor attack strategy in FL that dynamically constructs backdoor objectives by optimizing a backdoor trigger, making backdoor data have minimal effect on model updates. We provide theoretical justifications for DPOT's attacking principle and display experimental results showing that DPOT, via only a data-poisoning attack, effectively undermines state-of-the-art defenses and outperforms existing backdoor attack techniques on various datasets. |
| title | Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.06206 |