In FL-OA, the server collaborates with third-party organization that holds an additional root dataset to perform outsourced auditing, thereby enabling the server to achieve robust aggregation without strong assumptions, demonstrating that FL-OA outperforms existing defense methods against Byzantine attacks.
Abstract
Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data. However, due to its distributed nature, FL is vulnerable to Byzantine attacks. Existing defense methods rely on strong assumptions, such as the proportion of malicious devices not exceeding 50\%, or the server having an additional root dataset that matches the training task. Moreover, they show limited efficacy as they overlook $(i)$ the divergence among benign updates and $(ii)$ the curse of dimensionality involved in comparing two high-dimensional updates. To solve these concerns, we propose FL-OA, a Byzantine-robust federated learning framework utilizing outsourced auditing. In FL-OA, the server collaborates with third-party organization that holds an additional root dataset to perform outsourced auditing, thereby enabling the server to achieve robust aggregation without strong assumptions. Additionally, FL-OA introduces a gradient ascent step and a correction term during local training to mitigate the divergence among benign updates, and designs a parameter importance indicator to extract critical parameters for auditing, alleviating the curse of dimensionality. We further provide a detailed theoretical analysis of FL-OA. Extensive experiments demonstrate that FL-OA outperforms existing defense methods against Byzantine attacks.
A review of federated learning through a structured taxonomy that covers its core architectural paradigms, major learning types, model training approaches, and aggregation mechanisms, and analyzes the principal challenges confronting FL, including privacy and security risks, statistical and system heterogeneity, communication constraints, and global model divergence.
Mahdiyeh Velaei, Hosna Ghahramani, Ali Ghaffari et al.· Cluster Computing· 0 citations
The rapid adoption of Internet of Things (IoT) devices has accelerated the need for privacy-preserving machine learning techniques, such as federated learning (FL). However, the decentralized and collaborative nature of FL makes it vulnerable to backdoor attacks, where adversaries locally update their malicious models before contributing to the global aggregation, subtly injecting backdoors without degrading the normal performance. An affected model behaves as expected during regular operations but exhibits malicious behavior when an embedded trigger is presented. In this article, we propose a novel self-supervised contrastive-learning-based approach to detect and mitigate backdoor attacks in FL within IoT environments. Unlike conventional reverse-engineering methods that iterate through each class in the dataset to reconstruct triggers, our approach directly regenerates triggers from compromised global models without class iteration. This is achieved by comparing last-layer feature representations of a potentially compromised model with those of a relatively clean model under the guidance of contrastive loss. The reverse-engineered trigger is then leveraged to patch the global model and remove the backdoors. We evaluate our method on three benchmark datasets under two federated backdoor attack scenarios, simulating IoT device collaborations. Extended experiments are also conducted on a transformer-based model and two mitigation methods to assess the robustness of our approach. Our results demonstrate that while traditional reverse-engineering techniques are effective in centralized settings, they struggle to detect backdoors in FL. Comparatively, our method is resilient against backdoor attacks across various settings. In addition, our method is more time-efficient because of its capability of generating the backdoor trigger directly without iterating through all classes.
Hal Ferguson, Rui Ning, Hongyi Wu et al.· IEEE Internet of Things Jour...· 0 citations
FedSentinel is presented, a novel Byzantine-resilient federated learning framework that combines cryptographic gradient attestation with adaptive trust-weighted aggregation to protect against coordinated model-poisoning attacks, which are among the most serious challenges.
Abdullah Abdulkarim Alnajim· Electronics· 0 citations
A ablation indicates that removing MAD filtering substantially reduces class-balanced performance on HAM10000 under noise injection, and a diagnostic analysis shows that the cosine-distance signal separates benign and malicious updates strongly under noise injection but weakly under sign flipping.
Tae-Wook Kang, Ji-Woo Park, Chulyoung Park et al.· IEEE Access· 0 citations
Federated learning has gained significant attention for its ability to train models without directly accessing raw data. However, the malicious server may falsify the aggregation results to save computational resources. While existing verifiable federated learning methods can validate the results, they exacerbate communication bottlenecks and fail to achieve collusion-resistant verification in the absence of a trusted authority. In this paper, we propose CVFL-Pro, a collusion-resistant verification federated learning framework with adaptive communication optimization. Specifically, we utilize a mask and Shamir’s secret sharing for privacy protection, and it is robust against client dropout. We combine a lightweight MAC scheme and auxiliary nodes to achieve efficient verifiability. Furthermore, we design an adaptive communication optimization algorithm (AOTop-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>), which dynamically adjusts the compression rate <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> based on the gradient magnitude and the gradient variation between rounds, ensuring optimal performance with minimal cost. Finally, we instantiate CVFL-Pro and prove its correctness and security against collusion by up to <inline-formula> <tex-math notation="LaTeX">$|N|-2$ </tex-math></inline-formula> clients (where <inline-formula> <tex-math notation="LaTeX">$|N|$ </tex-math></inline-formula> is the total number of clients) and the server. Extensive evaluations on MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate that CVFL-Pro reduces communication overhead by 58.07% compared to the optimal Top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and 95.81% compared to SecAgg. Experiments show that CVFL-Pro reduces communication overhead by up to 95.81% while maintaining accuracy. It dynamically adjusts compression and achieves efficient storage, requiring only 830.16KB compared to 1952.62KB in SecAgg.
Ying Gao, Xiaofeng Chen, Huanghao Deng et al.· IEEE Transactions on Informa...· 0 citations
This work proposes CAEBA (Conditional AutoEncoder Backdoor Attack), a dynamic hidden backdoor framework that uses a conditional autoencoder to generate target-aware and visually stealthy triggers while progressively implanting the backdoor through federated optimization.
