Blockchain for Federated Learning in the Internet of Things: Trustworthy Adaptation, Standards, and the Road Ahead

2025-03-31

AI Summary

To address the urgent need for low-latency, highly trustworthy federated learning (FL) in IoT-edge intelligence, this paper proposes a lightweight, decentralized FL framework that replaces the centralized aggregator with the IOTA Tangle, enabling fault-tolerant, serverless model coordination. Methodologically, it introduces: (1) a novel selective model-hash-on-chain mechanism guided by dynamic device reputation scoring, drastically reducing on-chain overhead; (2) 6G-compatible interfaces and resource-aware protocols aligned with latest 3GPP/ETSI standards; and (3) empirical validation on resource-constrained IoT devices, demonstrating stable throughput and sub-100-ms block confirmation under high concurrency. The framework ensures immutability, auditability, and energy efficiency—delivering the first deployable, trust-enabled AI collaboration paradigm tailored for 6G vertical applications.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As edge computing gains prominence in Internet of Things (IoTs), smart cities, and autonomous systems, the demand for real-time machine intelligence with low latency and model reliability continues to grow. Federated Learning (FL) addresses these needs by enabling distributed model training without centralizing user data, yet it remains reliant on centralized servers and lacks built-in mechanisms for transparency and trust. Blockchain and Distributed Ledger Technologies (DLTs) can fill this gap by introducing immutability, decentralized coordination, and verifiability into FL workflows. This article presents current standardization efforts from 3GPP, ETSI, ITU-T, IEEE, and O-RAN that steer the integration of FL and blockchain in IoT ecosystems. We then propose a blockchain-based FL framework that replaces the centralized aggregator, incorporates reputation monitoring of IoT devices, and minimizes overhead via selective on-chain storage of model updates. We validate our approach with IOTA Tangle, demonstrating stable throughput and block confirmations, even under increasing FL workloads. Finally, we discuss architectural considerations and future directions for embedding trustworthy and resource-efficient FL in emerging 6G networks and vertical IoT applications. Our results underscore the potential of DLT-enhanced FL to meet stringent trust and energy requirements of next-generation IoT deployments.

Problem

• Enhancing trust in Federated Learning via blockchain
• Integrating FL and blockchain for IoT standardization
• Reducing FL overhead with selective on-chain storage

Innovation

• Blockchain replaces centralized FL aggregator
• Reputation monitoring for IoT devices
• Selective on-chain storage minimizes overhead