Temporal Analysis of Adversarial Attacks in Federated Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912193984856064 |
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| author | Mapakshi, Rohit Akther, Sayma Stamp, Mark |
| author_facet | Mapakshi, Rohit Akther, Sayma Stamp, Mark |
| contents | In this paper, we experimentally analyze the robustness of selected Federated Learning (FL) systems in the presence of adversarial clients. We find that temporal attacks significantly affect model performance in the FL models tested, especially when the adversaries are active throughout or during the later rounds. We consider a variety of classic learning models, including Multinominal Logistic Regression (MLR), Random Forest, XGBoost, Support Vector Classifier (SVC), as well as various Neural Network models including Multilayer Perceptron (MLP), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our results highlight the effectiveness of temporal attacks and the need to develop strategies to make the FL process more robust against such attacks. We also briefly consider the effectiveness of defense mechanisms, including outlier detection in the aggregation algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Temporal Analysis of Adversarial Attacks in Federated Learning Mapakshi, Rohit Akther, Sayma Stamp, Mark Machine Learning Cryptography and Security In this paper, we experimentally analyze the robustness of selected Federated Learning (FL) systems in the presence of adversarial clients. We find that temporal attacks significantly affect model performance in the FL models tested, especially when the adversaries are active throughout or during the later rounds. We consider a variety of classic learning models, including Multinominal Logistic Regression (MLR), Random Forest, XGBoost, Support Vector Classifier (SVC), as well as various Neural Network models including Multilayer Perceptron (MLP), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our results highlight the effectiveness of temporal attacks and the need to develop strategies to make the FL process more robust against such attacks. We also briefly consider the effectiveness of defense mechanisms, including outlier detection in the aggregation algorithm. |
| title | Temporal Analysis of Adversarial Attacks in Federated Learning |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2501.11054 |