International Journal of Computational Science and Digital Innovation (IJCSDI)

ORIGINAL RESEARCH•Vol. 1, Issue 1 (2027)•pp. 16-28

DOI: 10.5555/IJCSDI.2027.0002

Fault-Tolerant Consensus for Decentralized Edge Intelligence using Zero-Knowledge Proofs

Prof. Kenji Sato [DEMO]

Modern distributed edge applications demand trustless coordination among heterogeneous compute nodes with minimal communication overhead. We introduce an asymmetric zero-knowledge consensus protocol optimized for constrained devices. Our empirical evaluation shows consensus latency below 18ms on ARM-based cluster nodes.

Edge IntelligenceConsensus ProtocolZero-Knowledge ProofsDistributed SystemsIoT

Published Jan 15, 2027View Article & Citations

ORIGINAL RESEARCH•Vol. 1, Issue 1 (2027)•pp. 1-15

DOI: 10.5555/IJCSDI.2027.0001

Privacy-Preserving Federated Learning via Adaptive Gradient Perturbation in Edge Computing Networks

Dr. Evelyn Author [DEMO], Dr. Liam Zhang [DEMO]

Federated learning enables decentralized model training across distributed clients without exposing raw local data. However, vulnerability to gradient inversion attacks remains a primary bottleneck in edge deployments. In this paper, we propose an adaptive gradient perturbation mechanism with dynamic differential privacy guarantees. Experiments demonstrate a 41% reduction in privacy leakage with less than 1.2% degradation in model convergence across standard vision benchmarks.

Federated LearningEdge ComputingDifferential PrivacyGradient InversionNeural Optimization

Published Jan 15, 2027View Article & Citations

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Fault-Tolerant Consensus for Decentralized Edge Intelligence using Zero-Knowledge Proofs

Privacy-Preserving Federated Learning via Adaptive Gradient Perturbation in Edge Computing Networks