toplogo
Sign In

Hybrid Knowledge Distillation for Robust and Accurate Federated Learning


Core Concepts
Hybrid knowledge distillation approach that applies distillation at both the final layer and a shallow layer of the client model to mitigate the vulnerability of knowledge distillation-based federated learning techniques to model poisoning attacks.
Abstract

The paper presents a case study to reveal a critical vulnerability in knowledge distillation (KD)-based federated learning (FL) techniques. It shows that while these techniques effectively improve performance under high data heterogeneity, they inadvertently cause higher accuracy degradation under model poisoning attacks, a phenomenon termed "attack amplification".

The authors first provide empirical evidence and theoretical reasoning to explain why KD-based techniques like FedNTD and MOON amplify the impact of model poisoning attacks. They show that the very mechanisms that improve performance in benign conditions also make the models more vulnerable to adversarial attacks.

To address this issue, the authors propose Hybrid Knowledge Distillation for Robust and Accurate FL (HYDRA-FL), a novel technique that applies KD-loss at both the final layer and a shallow layer of the client model via an auxiliary classifier. This hybrid approach reduces the impact of poisoning on the client model by preventing over-reliance on final layer alignment.

The authors adapt HYDRA-FL to FedNTD and MOON, and their extensive experiments across three datasets show that HYDRA-FL significantly boosts accuracy over the baselines in attack settings while maintaining performance in benign settings.

edit_icon

Customize Summary

edit_icon

Rewrite with AI

edit_icon

Generate Citations

translate_icon

Translate Source

visual_icon

Generate MindMap

visit_icon

Visit Source

Stats
"Data heterogeneity among Federated Learning (FL) users poses a significant challenge, resulting in reduced global model performance." "Besides data heterogeneity, FL also faces the issue of Byzantine robustness, where untrusted clients can inject poisoned models into the aggregator by altering client data (data poisoning) or client models (model poisoning)."
Quotes
"KD-based techniques effectively improve performance under high heterogeneity, they inadvertently cause higher accuracy degradation under model poisoning attacks (known as attack amplification)." "The very mechanisms that improve performance in benign conditions (increasing β and μ) also make the models more vulnerable to adversarial attacks."

Deeper Inquiries

How can HYDRA-FL be extended to handle other types of attacks beyond model poisoning, such as data poisoning?

HYDRA-FL, which primarily addresses model poisoning attacks through its hybrid knowledge distillation approach, can be extended to handle data poisoning attacks by incorporating additional mechanisms that focus on the integrity of the data shared by clients. Data poisoning attacks occur when malicious clients manipulate their local datasets to degrade the performance of the global model. To extend HYDRA-FL for this purpose, several strategies can be employed: Robust Aggregation Techniques: Implementing robust aggregation methods that can identify and mitigate the influence of poisoned data during the model update phase. Techniques such as trimmed mean or median-based aggregation can help reduce the impact of outlier updates from malicious clients. Data Validation Mechanisms: Introducing data validation protocols that assess the quality and integrity of the data before it is used for training. This could involve anomaly detection algorithms that flag suspicious data patterns or inconsistencies. Client Reputation Systems: Establishing a reputation system for clients based on their historical behavior. Clients that consistently provide high-quality updates can be weighted more heavily in the aggregation process, while those with a history of providing poor or malicious updates can be down-weighted or excluded. Enhanced Distillation Loss Functions: Modifying the distillation loss functions to include penalties for deviations in data distribution. This could involve aligning the data distributions of local models with the expected global distribution, thereby reducing the risk of data poisoning. By integrating these strategies, HYDRA-FL can enhance its robustness against data poisoning attacks while maintaining its effectiveness against model poisoning.

What are the potential trade-offs or limitations of the shallow distillation approach used in HYDRA-FL, and how can they be further addressed?

The shallow distillation approach in HYDRA-FL, while effective in mitigating attack amplification, presents several potential trade-offs and limitations: Reduced Learning Capacity: By focusing on shallow layers for distillation, there is a risk that the model may not fully capture complex patterns that are typically learned in deeper layers. This could lead to a decrease in overall model performance, especially in tasks requiring high-level feature extraction. Increased Complexity: Introducing auxiliary classifiers and shallow distillation layers adds complexity to the model architecture. This can lead to increased computational overhead and longer training times, which may not be feasible in resource-constrained environments. Balancing Distillation Coefficients: The effectiveness of shallow distillation heavily relies on the careful tuning of the distillation coefficients (β and γ). Improper tuning can lead to suboptimal performance, either by overemphasizing the shallow distillation at the expense of the final layer or vice versa. To address these limitations, several strategies can be implemented: Adaptive Distillation Coefficients: Developing adaptive mechanisms that dynamically adjust the distillation coefficients based on the training phase or the observed performance can help maintain a balance between shallow and deep layer learning. Layer-wise Training Strategies: Implementing layer-wise training strategies that allow for independent training of shallow and deep layers can help the model learn complex representations while still benefiting from the robustness of shallow distillation. Regularization Techniques: Applying regularization techniques to prevent overfitting in shallow layers can help maintain the model's generalization capabilities while leveraging the robustness offered by shallow distillation. By addressing these trade-offs, HYDRA-FL can enhance its performance and robustness in various scenarios.

Could HYDRA-FL's principles be applied to other machine learning paradigms beyond federated learning to enhance robustness against adversarial attacks?

Yes, the principles underlying HYDRA-FL can be effectively applied to other machine learning paradigms beyond federated learning to enhance robustness against adversarial attacks. The core concepts of hybrid knowledge distillation and shallow distillation can be adapted to various contexts, including: Centralized Machine Learning: In traditional centralized machine learning settings, the hybrid distillation approach can be utilized to improve model robustness against adversarial examples. By distilling knowledge from a robust model (teacher) to a vulnerable model (student) while incorporating shallow distillation, the student model can learn to resist adversarial perturbations. Transfer Learning: In transfer learning scenarios, where a model is pre-trained on a large dataset and fine-tuned on a smaller, task-specific dataset, the principles of HYDRA-FL can be employed to ensure that the fine-tuned model retains robustness against adversarial attacks. Shallow distillation can help the model maintain essential features learned from the pre-trained model while adapting to the new task. Ensemble Learning: The hybrid distillation approach can be integrated into ensemble learning frameworks, where multiple models are trained and combined to improve performance. By applying shallow distillation across the ensemble, individual models can learn from each other while enhancing their robustness against adversarial attacks. Reinforcement Learning: In reinforcement learning, where agents learn from interactions with the environment, the principles of HYDRA-FL can be adapted to ensure that agents learn robust policies. By distilling knowledge from a robust policy (teacher) to a less robust policy (student) while incorporating shallow distillation, agents can improve their resilience to adversarial perturbations in the environment. By leveraging the principles of HYDRA-FL, various machine learning paradigms can enhance their robustness against adversarial attacks, leading to more reliable and secure models across different applications.
0
star