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…

UniDetect is a novel LLM-driven method that detects cross-chain cryptocurrency fraud by generating generalized transaction summaries, significantly outperforming existing detection techniques across m…

SCAFDS introduces a novel, seven-stage graph attention system that models fraud propagation using co-occurrence edge features and generates forensically traceable SAR narratives, significantly improvi…

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

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…

The paper proposes DIST-FL, a distributed system using multiple TEEs and an append-only ledger to enhance the security and robustness of federated learning aggregation against server-side adversaries.

The paper proposes an Institutional Coherence Index (ICC) regularization method for federated learning in intrusion detection, demonstrating superior performance by weighting local models based on ins…

The paper proposes a novel structural invariant approach, derived from the economic constraints of fraud, that amplifies weak, low-precision signals into highly accurate fraud detections without requi…

FedAttr introduces a novel client-level attribution protocol for Federated Learning (FL) that accurately identifies which clients trained on watermarked data while maintaining strong privacy guarantee…

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…

This paper proposes a unified, lifecycle-centric framework and a detailed taxonomy to survey and analyze novel, finance-specific attack surfaces and vulnerabilities in AI systems used within the finan…

The paper proposes FL-PBM, a novel pre-training defense mechanism for federated learning that proactively filters poisoned data using a multi-stage process, significantly reducing backdoor attack succ…

This paper proposes a comprehensive framework utilizing AI and machine learning to enhance cybersecurity and mitigate fraud risks in the emerging field of cardless artificial intelligence banking.

FedEDAuth is a lightweight, embedding-level authentication framework that enhances federated learning for counterfeit IC detection by identifying and filtering malicious participants before model aggr…

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…

The paper proposes AdaBFL, a multi-layer defensive adaptive aggregation method that enhances Byzantine-robust federated learning by adaptively adjusting defense weights to counter complex poisoning at…

The paper proposes IntraShuffler, a novel privacy-preserving middleware defense that enables gradient shuffling in Heterogeneous Differential Privacy Federated Learning (HDP-FL) systems, significantly…

The paper proposes IntraShuffler, a novel privacy-preserving middleware defense that enables gradient shuffling in Heterogeneous Differential Privacy Federated Learning (HDP-FL) while maintaining the…

This paper enhances Federated Learning privacy by integrating two key protection mechanisms—masking and RSA encapsulation—into Hybrid Homomorphic Encryption (HHE) to secure against malicious clients.

This paper proposes and evaluates the integration of Federated Learning and blockchain technology over cloud-edge infrastructure to enhance data privacy and security for decentralized AI applications.