The paper proposes a novel decentralized framework that uses blockchain and Multi-task Peer Prediction to incentivize and manage the computationally intensive process of Federated Learning.

TITAN-FedAnil+ is a trust-based, adaptive blockchain federated learning framework designed for resource-constrained intelligent enterprises, significantly improving robustness and resource efficiency.

This paper synthesizes the emerging field of blockchain and AI for securing intelligent networks by providing a comprehensive taxonomy, integration patterns, and an evaluation blueprint.

This review comprehensively analyzes state-of-the-art decentralized trust and security mechanisms, concluding that while these approaches enhance privacy and resilience for IoT edge networks, challeng…

This paper proposes and evaluates a federated deep learning framework using autoencoders for lightweight, privacy-preserving, and scalable real-time anomaly detection in resource-constrained IoT netwo…

This paper introduces a unified threat model and evaluation framework to systematically compare privacy-preserving techniques for distributed learning in IoT systems, highlighting the trade-off betwee…

The paper proposes Federated Computing as Code (FCaC), a declarative architecture that enforces sovereignty-critical constraints in federated systems by compiling authority into cryptographically veri…

This paper proposes a decentralized, blockchain-based protocol using both public and private blockchains to enhance the cyber resilience and security of IoT data transfer for digital building twins an…

The paper systematizes the interaction between autonomous AI agents and blockchain platforms using a bidirectional trust framework, identifying significant gaps in current standards and proposing a ta…

The paper proposes Family-Grouped Hierarchical Federated Learning (Family-FL) combined with a highly optimized Tiny CNN-LSTM model to enable privacy-preserving ECG monitoring on ultra-resource-constra…

This systematic review analyzes the bidirectional integration of AI and DLT, finding that while research is growing, most studies neglect cross-layer co-design and fail to demonstrate production-scale…

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 a comprehensive, dual-layer architectural framework for AI identification and traceability, ensuring continuous accountability and regulatory oversight throughout the entire lifecyc…

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

This paper proposes a comprehensive federated learning workflow that enhances privacy and robustness by integrating personalized differential privacy budgets and client drift detection, achieving bett…

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…

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…

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

This paper analyzes digital privacy risks in IoT ecosystems, proposing a comprehensive framework (AURA-IoT) and taxonomy to mitigate threats using advanced privacy-enhancing technologies.