The content introduces an innovative method to enhance security in Federated Learning by combining consensus-based validation with adaptive thresholding. The approach aims to mitigate label-flipping attacks and maintain the integrity of global models. By conducting experiments on benchmark datasets like CIFAR-10 and MNIST, the study demonstrates the effectiveness of the proposed algorithm. The research contributes significantly to improving security measures in FL systems, offering a practical solution for real-world applications.
The paper discusses the challenges faced by FL systems, particularly regarding security vulnerabilities such as adversarial attacks like label-flipping. Traditional defense mechanisms have limitations in addressing sophisticated forms of manipulation, leading to the proposal of a novel consensus-based label verification algorithm with adaptive thresholding. This approach ensures that only validated updates are integrated into the global model, enhancing security against adversarial threats.
Furthermore, the study delves into related work on FL development, security vulnerabilities, and advancements made in safeguarding distributed systems. It explores innovative approaches like blockchain integration for trust enhancement and highlights the need for continuous innovation in defense mechanisms due to FL's dynamic nature.
The theoretical analysis presented establishes convergence properties of the algorithm under standard FL settings with convex loss functions. Additionally, empirical validation using MNIST and CIFAR-10 datasets confirms the robustness and adaptability of the proposed approach against adversarial attacks.
Overall, this research contributes significantly to advancing security measures in Federated Learning systems by introducing a novel defense mechanism that addresses critical gaps in current strategies while paving the way for future investigations into scalable and efficient defense mechanisms.
To Another Language
from source content
arxiv.org
Principais Insights Extraídos De
by Zahir Alsula... às arxiv.org 03-11-2024
https://arxiv.org/pdf/2403.04803.pdfPerguntas Mais Profundas