HASSLE: a Self-Supervised Learning Enhanced Hijacking Attack on Vertical Federated Learning

Vertical Federated Learning VFL enables an orchestrating active party to perform a machine learning task by cooperating with passive parties that provide additional task-related features for the same training data entities. While prior research has leveraged the privacy vulnerability of VFL to...

AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing

Federated Learning FL faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environments. Recent approaches either sacrifice formal privacy guarantees, incur high overheads, or overlook quantum-enhanced...

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning

As IoT ecosystems continue to expand across critical sectors, they have become prominent targets for increasingly sophisticated and large-scale malware attacks. The evolving threat landscape, combined with the sensitive nature of IoT-generated data, demands detection frameworks that are both...

PROTEAN: Federated Intrusion Detection in Non-IID Environments through Prototype-Based Knowledge Sharing

In distributed networks, participants often face diverse and fast-evolving cyberattacks. This makes techniques based on Federated Learning FL a promising mitigation strategy. By only exchanging model updates, FL participants can collaboratively build detection models without revealing sensitive...

BackFed: an Efficient and Standardized Benchmark Suite for Backdoor Attacks in Federated Learning

Federated Learning FL systems are vulnerable to backdoor attacks, where adversaries train their local models on poisoned data and submit poisoned model updates to compromise the global model. Despite numerous proposed attacks and defenses, divergent experimental settings, implementation errors, a...

Empowering Digital Agriculture: a Privacy-Preserving Framework for Data Sharing and Collaborative Research

Data-driven agriculture, which integrates technology and data into agricultural practices, has the potential to improve crop yield, disease resilience, and long-term soil health. However, privacy concerns, such as adverse pricing, discrimination, and resource manipulation, deter farmers from...

SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning

Federated Learning FL has emerged as a leading paradigm for privacy-preserving distributed machine learning, yet the distributed nature of FL introduces unique security challenges, notably the threat of backdoor attacks. Existing backdoor strategies predominantly rely on end-to-end label...

Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning

Whitepaper called Secure Multi-Key Homomorphic Encryption With Application To Privacy-Preserving Federated Learning...

Network Structures As an Attack Surface: Topology-Based Privacy Leakage in Federated Learning

Federated learning systems increasingly rely on diverse network topologies to address scalability and organizational constraints. While existing privacy research focuses on gradient-based attacks, the privacy implications of network topology knowledge remain critically understudied. We conduct th...

Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?

Inherent communication noises have the potential to preserve privacy for wireless federated learning WFL but have been overlooked in digital communication systems predominantly using floating-point number standards, e.g., IEEE 754, for data storage and transmission. This is due to the potentially...

Federated Learning-Based Data Collaboration Method for Enhancing Edge Cloud AI System Security Using Large Language Models

With the widespread application of edge computing and cloud systems in AI-driven applications, how to maintain efficient performance while ensuring data privacy has become an urgent security issue. This paper proposes a federated learning-based data collaboration method to improve the security of...

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption

Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext...

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning

Despite federated learning FL's potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning CFL has emerged to address this challenge by partitioning users into clusters according to their...

A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives

Underwater target tracking technology plays a pivotal role in marine resource exploration, environmental monitoring, and national defense security. Given that acoustic waves represent an effective medium for long-distance transmission in aquatic environments, underwater acoustic target tracking h...

AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator

Whitepaper called AdRo-FL: Informed And Secure Client Selection For Federated Learning In The Presence Of Adversarial Aggregator...

AndroIDS : Android-Based Intrusion Detection System Using Federated Learning

The exponential growth of android-based mobile IoT systems has significantly increased the susceptibility of devices to cyberattacks, particularly in smart homes, UAVs, and other connected mobile environments. This article presents a federated learning-based intrusion detection framework called...

SecureFed: a Two-Phase Framework for Detecting Malicious Clients in Federated Learning

Federated Learning FL protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL...

CVE-2025-43863 vantage6 lacks brute-force protection on change password functionality

vantage6 is an open source framework built to enable, manage and deploy privacy enhancing technologies like Federated Learning and Multi-Party Computation. If attacker gets access to an authenticated session, they can try to brute-force the user password by using the change password functionality...

CVE-2025-43863 vantage6 lacks brute-force protection on change password functionality

vantage6 is an open source framework built to enable, manage and deploy privacy enhancing technologies like Federated Learning and Multi-Party Computation. If attacker gets access to an authenticated session, they can try to brute-force the user password by using the change password functionality...

vantage6 安全漏洞

vantage6 is a vantage6 open source priVAcy preserviNg federalTed leArningG infrastructure for Secure Insight eXchange. A security vulnerability exists in vantage6 versions prior to 4.11 that stems from the change password feature allowing unlimited attempts, which could lead to a brute force atta...