insight - Machine Learning - # Robustness of aggregators under label poisoning attacks in distributed learning

Core Concepts

The mean aggregator is more robust than state-of-the-art robust aggregators under label poisoning attacks in distributed learning, especially when the distributed data are sufficiently heterogeneous.

Abstract

The paper investigates the robustness of the mean aggregator and state-of-the-art robust aggregators under label poisoning attacks in distributed learning. The key insights are:
Under label poisoning attacks, the authors theoretically show that the learning error of the mean aggregator is optimal in order, regardless of the fraction of poisoned workers, when the distributed data are sufficiently heterogeneous.
In contrast, the learning errors of robust aggregators like TriMean, FABA, and centered clipping (CC) degrade significantly as the fraction of poisoned workers increases.
Numerical experiments on softmax regression, multi-layer perceptrons, and convolutional neural networks corroborate the theoretical findings, demonstrating the superior performance of the mean aggregator over robust aggregators under label poisoning attacks, especially in the non-i.i.d. data setting.
The authors also analyze the relationship between the heterogeneity of regular local gradients and the disturbance of poisoned local gradients, which provides insights into the effectiveness of the mean aggregator.
The results suggest that the mean aggregator should be preferred over robust aggregators in practical distributed learning scenarios with label poisoning attacks, especially when the distributed data are sufficiently heterogeneous.

Stats

"The disturbance of the poisoned local gradients, namely A, are bounded under both static label flipping and dynamic label flipping attacks."
"From i.i.d., mild non-i.i.d. to the non-i.i.d. case, the heterogeneity of regular local gradients characterized by ξ increases. Particularly, in the non-i.i.d. case, ξ is close to A under both static label flipping and dynamic label flipping attacks."

Quotes

"Surprisingly, we are able to show that the mean aggregator is more robust than the state-of-the-art robust aggregators in theory, given that the distributed data are sufficiently heterogeneous."
"The learning error of the mean aggregator is proven to be optimal in order."

Key Insights Distilled From

by Jie Peng,Wei... at **arxiv.org** 04-23-2024

Deeper Inquiries

To extend the analysis to the more challenging decentralized learning problem, one could consider scenarios where the communication between nodes is limited or unreliable. This could involve investigating the impact of communication delays, packet losses, or network partitions on the performance of the distributed learning algorithms. Additionally, exploring the resilience of the mean aggregator and robust aggregators in decentralized settings where nodes have varying computational capabilities or access to different subsets of data could provide valuable insights. Analyzing the convergence properties and robustness of these algorithms under such decentralized and heterogeneous conditions would be crucial for practical implementation in real-world distributed learning systems.

While the mean aggregator has shown to be more robust than robust aggregators under specific label poisoning attacks in certain scenarios, relying solely on the mean aggregator in practical distributed learning scenarios may have limitations. One potential drawback is that the mean aggregator may not be as effective in defending against more sophisticated or diverse types of attacks beyond label poisoning. In situations where Byzantine attacks or model poisoning attacks are prevalent, robust aggregators designed specifically to handle such adversarial behaviors may offer better protection and performance. Additionally, the mean aggregator may not be optimal in all cases of data heterogeneity or attack strengths, leading to suboptimal convergence rates or compromised model accuracy. Therefore, a balanced approach that leverages both the mean aggregator and robust aggregators based on the specific characteristics of the distributed learning environment and the nature of potential attacks would be more advisable.

The insights from this work can be applied to improve the robustness of distributed learning systems in other types of attacks beyond label poisoning by considering the underlying principles that make the mean aggregator more resilient in certain scenarios. For example, understanding the factors that contribute to the mean aggregator's effectiveness, such as the distribution of data across nodes and the level of heterogeneity, can guide the development of more adaptive and versatile robust aggregation techniques. By incorporating elements of the mean aggregator's resilience into the design of robust aggregators, researchers can enhance the overall security and performance of distributed learning systems against a wider range of adversarial threats. Additionally, exploring hybrid approaches that combine the strengths of both the mean aggregator and robust aggregators in a dynamic and adaptive manner could offer a comprehensive defense strategy against various types of attacks while optimizing learning efficiency and model accuracy.

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