Federated learning (FL) is known to be susceptible to model poisoning attacks in which malicious clients hamper the accuracy of the global model by sending manipulated model updates to the central server during the FL training process. Existing defenses mainly focus on Byzantine-robust FL aggregations, and largely ignore the impact of the underlying deep neural network (DNN) that is used to FL training. Inspired by recent findings on critical learning periods (CLP) in DNNs, where small gradient errors have irrecoverable impact on the final model accuracy, we propose a new defense, called a CLP-aware defense against poisoning of FL (DeFL). The key idea of DeFL is to measure fine-grained differences between DNN model updates via an easy-to-compute federated gradient norm vector (FGNV) metric. Using FGNV, DeFL simultaneously detects malicious clients and identifies CLP, which in turn is leveraged to guide the adaptive removal of detected malicious clients from aggregation. As a result, DeFL not only mitigates model poisoning attacks on the global model but also is robust to detection errors. Our extensive experiments on three benchmark datasets demonstrate that DeFL produces significant performance gain over conventional defenses against state-of-the-art model poisoning attacks.
CONTRA: Defending against Poisoning Attacks in Federated Learning
Federated learning (FL) is an emerging machine learning paradigm. With FL, distributed data owners aggregate their model updates to train a shared deep neural network collaboratively, while keeping the training data locally. However, FL has little control over the local data and the training process. Therefore, it is susceptible to poisoning attacks, in which malicious or compromised clients use malicious training data or local updates as the attack vector to poison the trained global model. Moreover, the performance of existing detection and defense mechanisms drops significantly in a scaled-up FL system with non-iid data distributions. In this paper, we propose a defense scheme named CONTRA to defend against poisoning attacks, e.g., label-flipping and backdoor attacks, in FL systems. CONTRA implements a cosine-similarity-based measure to determine the credibility of local model parameters in each round and a reputation scheme to dynamically promote or penalize individual clients based on their per-round and historical contributions to the global model. With extensive experiments, we show that CONTRA significantly reduces the attack success rate while achieving high accuracy with the global model. Compared with a state-of-the-art (SOTA) defense, CONTRA reduces the attack success rate by 70% and reduces the global model performance degradation by 50%.
- Award ID(s):
- 2014552
- Publication Date:
- NSF-PAR ID:
- 10294585
- Journal Name:
- European Symposium on Research in Computer Security
- Sponsoring Org:
- National Science Foundation
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