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.

翻译：随着边缘计算在物联网、智慧城市和自主系统中的重要性日益凸显，对低延迟、高可靠模型的实时机器智能需求持续增长。联邦学习通过分布式模型训练避免用户数据集中化来满足这些需求，但其仍依赖中心化服务器，且缺乏透明的内置信任机制。区块链与分布式账本技术通过为联邦学习工作流引入不可篡改性、去中心化协调与可验证性，能够填补这一空白。本文系统梳理了3GPP、ETSI、ITU-T、IEEE及O-RAN等组织当前推动物联网生态中联邦学习与区块链融合的标准化工作。进而提出一种基于区块链的联邦学习框架，该框架通过取代中心化聚合器、引入物联网设备信誉监控机制，并采用模型更新的选择性链上存储以降低开销。我们基于IOTA Tangle验证了所提方案，证明即使在联邦学习工作负载持续增加的情况下，系统仍能保持稳定的吞吐量与区块确认效率。最后，我们探讨了在新兴6G网络与垂直物联网应用中嵌入可信且资源高效的联邦学习的架构考量与未来方向。研究结果凸显了DLT增强型联邦学习在满足下一代物联网部署对可信度与能效的严苛要求方面的巨大潜力。