Federated Learning, Blockchain, and Zero-Trust Security: A Unified Framework for Privacy-Preserving Intelligent Computing
Abstract
ABSTRACT
The rapid escalation of decentralized intelligent computing, driven by Internet
of Things (IoT) expansion and edge AI deployments, has intensified pressing
concerns regarding user data privacy, model integrity, and unauthorized
system access. Traditional centralized machine learning paradigms
necessitate the aggregation of sensitive data onto unified servers, exposing
critical infrastructures to single point of failure vulnerabilities, data breaches,
and regulatory non-compliance. While Federated Learning (FL) mitigates
privacy risks by training statistical models locally on distributed edge nodes, it
remains fundamentally susceptible to inference attacks, model poisoning,
centralized aggregator hijacking, and rogue node insertion. To overcome
these critical bottlenecks, this paper proposes a novel, highly resilient, unified
framework integrating Federated Learning, Blockchain technology, and Zero
Trust Security Architecture (ZTA). In the proposed framework, Zero-Trust
enforces continuous dynamic authentication, micro-segmentation, and least
privilege verification across all participating edge nodes and model update
flows, effectively eliminating implicit trust. Simultaneously, a decentralized
permissioned blockchain replaces the centralized parameter server,
establishing an immutable, tamper-resistant ledger for gradient verification,
smart-contract-driven aggregation, and auditable governance. Furthermore,
local differential privacy and secure multi-party computation mechanisms are
embedded to counteract reconstruction and membership inference threats. We
present a rigorous system design, formal mathematical formulation,
comprehensive security parameter evaluation, and comparative performance
analysis. Extensive simulations demonstrate that the proposed unified
framework achieves superior resilience against up to 35% malicious
poisoning nodes, maintains high model convergence accuracy (94.2%), and introduces minimal latency overhead (28.2 seconds per communication round)
relative to traditional blockchain-FL integrations. This review synthesizes
state-of-the-art developments, delineates critical research gaps, and outlines
practical application paradigms in healthcare, smart grids, and autonomous
vehicular networks.
KEYWORDS: Federated Learning; Blockchain Technology; Zero-Trust
Security; Privacy-Preserving AI; Edge Computing; Smart Contracts; Model
Poisoning Mitigation.
The rapid escalation of decentralized intelligent computing, driven by Internet
of Things (IoT) expansion and edge AI deployments, has intensified pressing
concerns regarding user data privacy, model integrity, and unauthorized
system access. Traditional centralized machine learning paradigms
necessitate the aggregation of sensitive data onto unified servers, exposing
critical infrastructures to single point of failure vulnerabilities, data breaches,
and regulatory non-compliance. While Federated Learning (FL) mitigates
privacy risks by training statistical models locally on distributed edge nodes, it
remains fundamentally susceptible to inference attacks, model poisoning,
centralized aggregator hijacking, and rogue node insertion. To overcome
these critical bottlenecks, this paper proposes a novel, highly resilient, unified
framework integrating Federated Learning, Blockchain technology, and Zero
Trust Security Architecture (ZTA). In the proposed framework, Zero-Trust
enforces continuous dynamic authentication, micro-segmentation, and least
privilege verification across all participating edge nodes and model update
flows, effectively eliminating implicit trust. Simultaneously, a decentralized
permissioned blockchain replaces the centralized parameter server,
establishing an immutable, tamper-resistant ledger for gradient verification,
smart-contract-driven aggregation, and auditable governance. Furthermore,
local differential privacy and secure multi-party computation mechanisms are
embedded to counteract reconstruction and membership inference threats. We
present a rigorous system design, formal mathematical formulation,
comprehensive security parameter evaluation, and comparative performance
analysis. Extensive simulations demonstrate that the proposed unified
framework achieves superior resilience against up to 35% malicious
poisoning nodes, maintains high model convergence accuracy (94.2%), and introduces minimal latency overhead (28.2 seconds per communication round)
relative to traditional blockchain-FL integrations. This review synthesizes
state-of-the-art developments, delineates critical research gaps, and outlines
practical application paradigms in healthcare, smart grids, and autonomous
vehicular networks.
KEYWORDS: Federated Learning; Blockchain Technology; Zero-Trust
Security; Privacy-Preserving AI; Edge Computing; Smart Contracts; Model
Poisoning Mitigation.