Improving Federated Learning Robustness with Logits Calibration on Non-IID Data

Core Concepts

The author aims to enhance the robustness of federated learning models against adversarial attacks and non-IID challenges by introducing a novel logits calibration strategy under the federated adversarial training framework. This approach improves model performance by addressing class imbalances and biases between local and global models.

Abstract

The content discusses the vulnerability of federated learning (FL) to adversarial examples (AEs) and non-independent and identically distributed (non-IID) data challenges. The proposed method, FedALC, combines adversarial training (AT) with logits calibration to improve model robustness. By adjusting logit weights based on class frequencies, the approach aims to mitigate biases in model training due to class imbalances. Experimental results on MNIST, Fashion-MNIST, and CIFAR-10 datasets demonstrate competitive natural and robust accuracy compared to baselines. The article outlines the standard FL process, introduces FAT as a defense mechanism against AEs, and details the calibrated local adversarial training phase. Implementation details, hyperparameters, metrics, performance comparisons across datasets, communication efficiency evaluations, and future work directions are discussed.

Stats

"Experimental results show that our proposal achieves significant performance gains in both natural accuracy and robust accuracy." "FedALC outperforms other baselines in most cases for both natural test accuracy and robust test accuracy." "Under FGSM attack, our approach still has an advantage over other baselines." "FedALC exhibits superior communication efficiency compared to other baselines." "Our proposal significantly surpasses the baselines as the number of iterations increases."

Quotes

Key Insights Distilled From

Towards Robust Federated Learning via Logits Calibration on Non-IID Data

by Yu Qiao,Apur... at arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02803.pdf

Deeper Inquiries

How can logits calibration be further optimized for different types of datasets

Logits calibration can be further optimized for different types of datasets by considering the specific characteristics and challenges present in each dataset. For instance, for datasets with severe class imbalances, a more sophisticated weighting scheme based on the frequency of occurrence of each class could be implemented to address this issue effectively. Additionally, incorporating adaptive strategies that dynamically adjust the calibration process based on the data distribution within each device can enhance the robustness and accuracy of models across various datasets. Furthermore, exploring advanced techniques such as meta-learning or reinforcement learning to optimize the logits calibration process according to dataset-specific features could lead to significant improvements in model performance.

What are potential implications of implementing FedALC in real-world edge computing scenarios

Implementing FedALC in real-world edge computing scenarios has several potential implications. Firstly, it can significantly enhance privacy preservation by allowing collaborative model training without exposing raw data from individual devices. This is crucial in sensitive applications where data confidentiality is paramount. Secondly, FedALC's ability to improve model robustness against adversarial attacks makes it well-suited for security-critical edge computing environments where threat mitigation is essential. Moreover, by addressing non-IID challenges through logits calibration, FedALC enables more accurate and reliable model training across heterogeneous edge devices, leading to better overall performance and efficiency in real-world deployments.

How might advancements in federated learning impact broader AI applications beyond edge networks

Advancements in federated learning facilitated by approaches like FedALC have far-reaching implications for broader AI applications beyond edge networks. One key impact is democratizing access to diverse and distributed data sources while maintaining privacy constraints—a critical consideration across industries such as healthcare, finance, and IoT. By enabling collaborative model training without centralizing sensitive information, federated learning opens up possibilities for developing more robust and generalizable AI models that reflect real-world variability better than traditional centralized approaches. Moreover, the scalability inherent in federated learning allows for efficient utilization of resources across decentralized systems—potentially revolutionizing large-scale AI deployment scenarios like smart cities or autonomous vehicles. Furthermore, by enhancing model resilience against adversarial attacks through techniques like adversarial training within a federated framework, federated learning contributes towards building trustworthy AI systems capable of operating securely even amidst evolving threats and challenges posed by malicious actors. These advancements pave the way for safer, more reliable AI applications with improved adaptability and performance across diverse domains beyond just edge networks alone.