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

Farhana Javed, Engin Zeydan, Josep Mangues-Bafalluy, Kapal Dev, Luis Blanco

TL;DR

The paper addresses the need for trustworthy, scalable FL in IoT by replacing centralized aggregators with a blockchain-based framework. It surveys standardization efforts from 3GPP, ETSI, ITU-T, IEEE, and O-RAN, and proposes a DLT-enabled FL architecture using a permissioned IOTA Tangle with a DLT-Adapter, DLT-Verifier, and DLT-Aggregator, complemented by reputation management and selective on-chain storage. The authors validate the approach with a PoC on IOTA, demonstrating stable throughput and predictable latency under increasing FL rounds, while off-chain storage (IPFS) preserves privacy and data efficiency. The work lays out architectural considerations and a roadmap toward trustworthy, energy-efficient FL in 6G-era IoT and vertical applications, highlighting standardization as a key enabler for interoperability and scalability.

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.

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

TL;DR

The paper addresses the need for trustworthy, scalable FL in IoT by replacing centralized aggregators with a blockchain-based framework. It surveys standardization efforts from 3GPP, ETSI, ITU-T, IEEE, and O-RAN, and proposes a DLT-enabled FL architecture using a permissioned IOTA Tangle with a DLT-Adapter, DLT-Verifier, and DLT-Aggregator, complemented by reputation management and selective on-chain storage. The authors validate the approach with a PoC on IOTA, demonstrating stable throughput and predictable latency under increasing FL rounds, while off-chain storage (IPFS) preserves privacy and data efficiency. The work lays out architectural considerations and a roadmap toward trustworthy, energy-efficient FL in 6G-era IoT and vertical applications, highlighting standardization as a key enabler for interoperability and scalability.

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.

Paper Structure

This paper contains 16 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Overview of Standardization Efforts and Current Challenges
  3. Standardization Efforts for FL in IoT Context
  4. Blockchain Standardization Efforts
  5. Challenges
  6. Blockchain-based FL in IoT: System Architecture and Workflow
  7. Integration of Blockchain in FL for IoT
  8. FL Model Integration of IoT Devices with Blockchain
  9. Reputation Management
  10. Lightweight Transactions
  11. Experimental Evaluations
  12. Simulation Results
  13. Discussion and Future Directions
  14. Discussion and Key Takeaways
  15. Future Directions
  16. ...and 1 more sections

Figures (3)

  • Figure 1: High-level view of system architecture for DLT-enabled trustworthy and FL for IoT.
  • Figure 2: Distribution of block processing time across different FL rounds—10, 30, and 50.
  • Figure 3: 6G network architecture integrating DLT, AI for trustworthy 6G networks services. The framework illustrates key functional modules, decentralized trust and orchestration components, and support for IoT vertical applications across user, control, and data planes.