The paper proposes a secure and verifiable aggregation scheme for Federated Learning using a non-colluding dual-server architecture and linear tags, which significantly enhances user privacy and reduc…

DisAgg introduces a novel secure aggregation protocol that uses a small committee of Aggregators to compute partial sums, achieving a significant speedup (4.6x) over previous state-of-the-art methods…

This paper argues that much of the existing research on Federated Learning (FL) security is based on idealized assumptions, and provides a practical evaluation framework showing that real-world attack…

TeeDAO introduces a novel three-layer framework that autonomously organizes and manages multiple heterogeneous Trusted Execution Environments (TEEs) to provide robust, distributed-trust systems with h…

SecureAFL introduces a robust framework to secure asynchronous Federated Learning against poisoning attacks by detecting anomalous updates, estimating missing client contributions, and using Byzantine…

FedFG introduces a robust federated learning framework using flow-matching generation to simultaneously enhance client privacy and defend against sophisticated poisoning attacks.

The paper proposes an optimized, end-to-end privacy-preserving framework for vertical federated learning by distributing aggregation roles across multiple servers using secure multiparty computation a…

EdgeDetect is a communication-efficient and privacy-preserving federated intrusion detection system that uses gradient binarization and homomorphic encryption to significantly reduce bandwidth usage w…

FedIDM introduces a novel federated learning framework that uses iterative distribution matching to achieve fast and stable convergence and maintain high model utility even when facing a large proport…

The paper introduces XFED, a novel non-collusive model poisoning attack that demonstrates the feasibility of compromising Federated Learning systems without requiring coordination among attackers, byp…

This paper analyzes label inference attacks in Vertical Federated Learning (VFL), demonstrating that existing attacks rely on feature-label distribution alignment, and proposes a zero-overhead defense…

CLAD is a federated learning framework that jointly performs anomaly detection and attack classification in heterogeneous IoT environments by combining clustered learning with a dual-mode architecture…

enclawed is a configurable, hard-fork hardening framework for AI assistant gateways that enforces strict security controls, verifiable trust, and auditable connectivity for regulated environments.

The paper introduces Dynamic Sharded Federated Learning (DSFL), a secure aggregation framework that significantly reduces communication overhead and enhances update verification for cross-institution…

The paper proposes PINA, a two-stage differentially private clustered federated learning framework that improves convergence and robustness by using low-rank adaptation and a normality-driven aggregat…

This paper empirically evaluates the effectiveness of Differential Privacy (DP) against Membership Inference Attacks (MIAs) in Federated Learning, demonstrating that a stacking attack strategy can det…

This paper presents a novel data-free Membership Inference Attack (MIA) that uses gradient inversion on Standard Cell Library Layouts (SCLLs) to reconstruct sensitive hardware images from intercepted…

The paper introduces 'Routing Hijacking,' a severe attack where malicious clients forge semantic profiles in Federated RAG systems to misroute target queries, and proposes a trust-aware post-routing f…

The paper proposes Byz-Clip21-SGD2M, a novel algorithm that achieves high-probability convergence guarantees for Federated Learning by integrating robust aggregation, double momentum, and clipping, re…

The paper introduces Landseer, a modular framework designed to systematically evaluate and compose multiple machine learning defenses to address complex, real-world security requirements.