Cybersecurity
Poisoning-Resilient Federated Learning for MEC-IoT Environments Using Blockchain
This paper proposes a novel blockchain-based architecture for Federated Learning (FL) in Multi-access Edge Computing (MEC) and Internet of Things (IoT) environments, specifically designed to mitigate security threats like data poisoning and Sybil attacks. By integrating a permissioned blockchain with Smart Contracts (SCs) and dual-factor authentication, the system ensures data integrity, secure node interactions, and transparent audit trails. Experimental validation demonstrates its effectiveness in maintaining optimal model performance and accuracy even under attack, while showing low resource consumption and minimal time overhead, making it practical for large-scale, heterogeneous MEC-IoT deployments.
Executive Impact: Key Metrics & Projections
Our solution significantly enhances the security posture of FL in MEC-IoT, reducing the impact of poisoning attacks on model performance by over 90% in worst-case scenarios, compared to unprotected systems. The blockchain integration adds minimal overhead, with bandwidth consumption increasing by only 6% for up to 200 clients and execution time overhead remaining below 7% for 20 clients. This ensures robust, scalable, and trustworthy AI operations in critical distributed environments.
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Reduction in Poisoning Attack Impact
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Blockchain Bandwidth Overhead (up to 200 clients)
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Blockchain ET Overhead (up to 20 clients)
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Baseline Performance Maintained (worst-case protected)
Deep Analysis & Enterprise Applications
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General Overview
Poisoning Mitigation Flow
Model Performance
Blockchain-FL Comparison
Real-World Impact
The proposed architecture introduces a modular and lightweight blockchain solution for Federated Learning (FL) in MEC-IoT environments. It leverages a permissioned Ethereum blockchain with a Proof-of-Authority (PoA) consensus algorithm and Smart Contracts (SCs) to ensure security and data integrity. Key features include a dual-factor authentication scheme for node registration, early detection and mitigation of data and model poisoning attacks through on-chain parameter and dataset hash validation, and a passive blockchain approach to minimize latency and resource consumption. This design offers a generalizable and scalable security layer specifically targeting pressing threats in FL environments.
Enterprise Process Flow
Server stores initial model parameters on blockchain
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Server sends parameters to clients
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Server stores global dataset hash
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Client obtains & verifies data hash
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Client stores subset data hash (before training)
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Client obtains & verifies model parameters (from server)
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Client obtains & verifies subset data hash (local)
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Client stores new valid local model hash
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Client returns local model parameters to server
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Optimal Model Performance (MNIST Dataset, No Attacks)
This metric highlights the benchmark F1-Score achievable with the MNIST dataset under ideal conditions (no attacks, no blockchain overhead). Our proposed solution aims to maintain performance as close to this optimal baseline as possible, even in the presence of sophisticated poisoning attacks.
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| Authentication |
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| Poisoning Mitigation |
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| Blockchain Role |
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| Targeted Threats |
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Real-World Impact: Smart City Analytics & Predictive Maintenance
In smart city deployments, our blockchain-based FL can secure real-time sensor data processing for AI-driven services. Adversaries often manipulate location data to distort statistical outcomes, severely affecting service utility and user privacy. Our solution's robust data integrity checks prevent such poisoning, ensuring reliable urban service recommendations and accurate predictions, even under constrained computational conditions.
For Industrial IoT (IIoT) systems, securing ML models used in predictive maintenance and fault detection is paramount. Poisoning attacks can disrupt these models, leading to unsafe operating conditions and costly downtime. By verifying model parameters and data hashes on-chain, our system protects against malicious updates, thereby maintaining the integrity and reliability of AI models critical for operational safety and efficiency in industrial settings.
Advanced ROI Calculator
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Implementation Roadmap
A structured approach to integrating blockchain-based, poisoning-resilient FL into your existing MEC-IoT infrastructure.
Phase 1: Needs Assessment & Pilot
Conduct a comprehensive analysis of current FL workflows, identify critical data sources and potential attack vectors. Deploy a small-scale pilot of the blockchain-based authentication and poisoning mitigation contracts in a controlled environment to validate core functionalities.
Phase 2: Architecture Integration & Customization
Integrate the permissioned Ethereum blockchain with PoA consensus into your MEC-IoT infrastructure. Customize Smart Contracts for specific data types and ML models. Develop communication interfaces for seamless interaction between IoT devices, edge servers, and the blockchain without significant resource overhead.
Phase 3: Scalability Testing & Full Deployment
Perform extensive scalability tests with increasing numbers of FL clients and varied attack scenarios to ensure performance remains robust. Gradually roll out the solution across your enterprise, providing training and support to operational teams to maximize adoption and security benefits.
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