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.

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

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

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.

SwarmHarness introduces a decentralized, incentive-aligned protocol enabling self-organizing compute swarms for AI tasks, eliminating the need for central coordinators or heavy blockchain infrastructu…

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 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…

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 empirical study of Pearl's cuPOW protocol demonstrates that the network's Proof-of-Useful-Work mechanism generates zero useful AI computation, instead causing economic harm and displacing legitim…

The paper demonstrates that for edge-native SLMs used in decentralized governance, simpler, intuitive reasoning (System 1) is significantly more robust and efficient than complex, iterative deliberati…

This paper analyzes 48 large, active DAOs on Ethereum and finds that common governance mechanisms like token registration, staking, and delegation systematically reinforce the centralization of voting…

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…

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 introduces LATTICE, a novel benchmark for evaluating how well crypto agents assist user decision-making, finding that different agents excel in different specific areas rather than having a…

This study uses a BERT-based LLM to analyze Discord sentiment and combines it with financial data to build a multi-modal model that significantly improves the prediction of Decentraland's MANA token p…

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

HadAgent introduces a decentralized AI serving system that replaces resource-intensive Proof-of-Work with Proof-of-Inference (PoI) to secure LLM agent operations and achieve fast, verifiable consensus…

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…