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.

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.

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 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…

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

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

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…

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 introduces ClawCoin, a novel tokenized, compute-cost-indexed unit of account designed to solve the problem of non-transferable compute costs in decentralized AI agent economies.

The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interp…

The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interp…

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…

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

CHRONOS is a hardware-assisted framework that significantly reduces the latency of secure federated learning by decoupling cryptographic key setup from the active training phase, while maintaining hig…

The paper proposes an Adaptive Neuro-Fuzzy Blockchain-AI Framework (ANFB-AI) that enhances FinTech security by combining blockchain immutability with adaptive AI to detect complex fraud in real-time.

The paper proposes a federated formal verification architecture that treats verification as a polyglot proof system, successfully validating it on complex production subsystems like a Raft consensus m…

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…