SureFED: Robust Federated Learning Framework with Uncertainty-Aware Inspection

Core Concepts

SureFED introduces a novel framework for robust federated learning by utilizing uncertainty quantification and local model evaluation to address vulnerabilities in existing methods.

Abstract

SureFED is a pioneering framework for robust federated learning that leverages uncertainty quantification and local model evaluation to combat various data and model poisoning attacks. The framework ensures robustness even in the presence of compromised clients, offers superior performance compared to state-of-the-art methods, and provides theoretical guarantees for decentralized linear regression settings.
The content discusses the challenges in federated learning due to vulnerabilities to adversarial attacks, introduces SureFED as a solution based on uncertainty-aware inspection, Bayesian models, and introspection procedures. It highlights the importance of local models in evaluating received updates and ensuring system integrity. Experimental results demonstrate SureFED's effectiveness against various attacks across different datasets.
Key points include:

Introduction of SureFED for robust federated learning
Utilization of uncertainty quantification and local model evaluation
Superior performance compared to existing defense methods
Theoretical guarantees for decentralized linear regression settings

Stats

SureFED achieves a final model accuracy matching benign training accuracy of 96% for MNIST, 73% for FEMNIST, and 71% for CIFAR10.
Backdoor accuracy of SureFED under Trojan attack is 24% for MNIST and 23% for FEMNIST datasets.
SureFED demonstrates consistent robustness against all examined datasets and attacks.
SureFED's model accuracy remains at 73% under Label-Flipping attack with incomplete or time-varying communication graphs.

Quotes

"SureFED leverages Bayesian models that provide model uncertainties and play a crucial role in the model evaluation process."
"Our framework exhibits robustness even when the majority of clients are compromised."
"SureFED demonstrates superior performance compared to the state of the art defense methods."

Key Insights Distilled From

SureFED

by Nasimeh Heyd... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2308.02747.pdf

Deeper Inquiries

How can federated learning frameworks like SureFED adapt to evolving adversarial tactics

Federated learning frameworks like SureFED can adapt to evolving adversarial tactics by incorporating robust defense mechanisms that leverage local information from clients. By utilizing uncertainty quantification in model evaluation and aggregation, SureFED can effectively detect and mitigate various data and model poisoning attacks. This approach allows the framework to remain resilient even when faced with new or sophisticated adversarial strategies. Additionally, SureFED's introspection process enables clients to evaluate their own models using clean local models, providing a reliable ground truth for identifying compromised nodes.

What are potential limitations or drawbacks of incorporating uncertainty quantification into federated learning models

Incorporating uncertainty quantification into federated learning models may introduce certain limitations or drawbacks. One potential drawback is the computational overhead associated with Bayesian modeling techniques used for estimating uncertainties in the model parameters. This additional complexity could lead to increased training times and resource requirements, impacting the scalability of the federated learning system. Moreover, uncertainty quantification methods may introduce challenges in interpreting and explaining model decisions due to the probabilistic nature of uncertainty estimates. Ensuring proper calibration of uncertainties and managing trade-offs between accuracy and robustness are critical considerations when implementing these techniques.

How might advancements in peer-to-peer federated learning impact broader applications beyond machine learning

Advancements in peer-to-peer federated learning have the potential to impact broader applications beyond machine learning by enabling decentralized collaboration among interconnected devices or entities. In fields such as healthcare, finance, IoT networks, and supply chain management, peer-to-peer federated learning can facilitate secure data sharing while preserving privacy-sensitive information locally on individual devices. This distributed approach enhances data security, reduces communication costs associated with centralized processing, and promotes collaborative decision-making across diverse stakeholders without compromising sensitive data privacy.