Layer-2 protocols can assist Ethereum's limited throughput, but globally broadcasting layer-2 data limits their scalability. The Danksharding evolution of Ethereum aims to support the selective distribution of layer-2 data, whose availability in the network is verified using randomized data availability sampling (DAS). Integrating DAS into Ethereum's consensus process is challenging, as pieces of layer-2 data must be disseminated and sampled within four seconds of the beginning of each consensus slot. No existing solution can support dissemination and sampling under such strict time bounds. We propose PANDAS, a practical approach to integrate DAS with Ethereum under Danksharding's requirements without modifying its protocols for consensus and node discovery. PANDAS disseminates layer-2 data and samples its availability using lightweight, direct exchanges. Its design accounts for message loss, node failures, and unresponsive participants while anticipating the need to scale out the Ethereum network. Our evaluation of PANDAS's prototype in a 1,000-node cluster and simulations for up to 20,000 peers shows that it allows layer-2 data dissemination and sampling under planetary-scale latencies within the 4-second deadline.
Due to the scalability and portability, the low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high dynamic mobility, and vulnerable to security threats, which may degrade the routing performance for data transmission. Hence, how to ensure the routing stability and security of LAINs is a challenge. In this paper, we focus on the routing process in LAINs with multiple UAV clusters and propose the blockchain-enabled zero-trust architecture to manage the joining and exiting of UAVs. Furthermore, we formulate the routing problem to minimize the end-to-end (E2E) delay, which is an integer linear programming and intractable to solve. Therefore, considering the distribution of LAINs, we reformulate the routing problem into a decentralized partially observable Markov decision process. With the proposed soft hierarchical experience replay buffer, the multi-agent double deep Q-network based adaptive routing algorithm is designed. Finally, simulations are conducted and numerical results show that the total E2E delay of the proposed mechanism decreases by 22.38\% than the benchmark on average.
This paper integrates Austrian capital theory with repeated game theory to examine strategic miner behaviour under different institutional conditions in blockchain systems. It shows that when protocol rules are mutable, effective time preference rises, undermining rational long-term planning and cooperative equilibria. Using formal game-theoretic analysis and Austrian economic principles, the paper demonstrates how mutable protocols shift miner incentives from productive investment to political rent-seeking and influence games. The original Bitcoin protocol is interpreted as an institutional anchor: a fixed rule-set enabling calculability and low time preference. Drawing on the work of Bohm-Bawerk, Mises, and Hayek, the argument is made that protocol immutability is essential for restoring strategic coherence, entrepreneurial confidence, and sustainable network equilibrium.
Siamak Abdi, Giuseppe Di Fatta, Atta Badii, Giancarlo Fortino
Blockchain is a decentralised, immutable ledger technology that has been widely adopted in many sectors for various applications such as cryptocurrencies, smart contracts and supply chain management. Distributed consensus is a fundamental component of blockchain, which is required to ensure trust, security, and integrity of the data stored and the transactions processed in the blockchain. Various consensus algorithms have been developed, each affected from certain issues such as node failures, high resource consumption, collusion, etc. This work introduces a fully decentralised consensus protocol, Blockchain Epidemic Consensus Protocol (BECP), suitable for very large and extreme-scale blockchain systems. The proposed approach leverages the benefits of epidemic protocols, such as no reliance on a fixed set of validators or leaders, probabilistic guarantees of convergence, efficient use of network resources, and tolerance to node and network failures. A comparative experimental analysis has been carried out with traditional protocols including PAXOS, RAFT, and Practical Byzantine Fault Tolerance (PBFT), as well as a relatively more recent protocol such as Avalanche, which is specifically designed for very large-scale systems. The results illustrate how BECP outperforms them in terms of throughput, scalability and consensus latency. BECP achieves an average of 1.196 times higher throughput in terms of consensus on items and 4.775 times better average consensus latency. Furthermore, BECP significantly reduces the number of messages compared to Avalanche. These results demonstrate the effectiveness and efficiency of fully decentralised consensus for blockchain technology based on epidemic protocols.
Web3 technologies have experienced unprecedented growth in the last decade, achieving widespread adoption. As various blockchain networks continue to evolve, we are on the cusp of a paradigm shift in which they could provide services traditionally offered by the Internet, but in a decentralized manner, marking the emergence of the Internet of Blockchains. While significant progress has been achieved in enabling interoperability between blockchain networks, existing solutions often assume that networks are already mutually aware. This reveals a critical gap: the initial discovery of blockchain networks remains largely unaddressed. This paper proposes a decentralized architecture for blockchain network discovery that operates independently of any centralized authority. We also introduce a mechanism for discovering assets and services within a blockchain from external networks. Given the decentralized nature of the proposed discovery architecture, we design an incentive mechanism to encourage nodes to actively participate in maintaining the discovery network. The proposed architecture implemented and evaluated, using the Substrate framework, demonstrates its resilience and scalability, effectively handling up to 130,000 concurrent requests under the tested network configurations, with a median response time of 5.5 milliseconds, demonstrating the ability to scale its processing capacity further by increasing its network size.
Cui Zhang, Maoxin Ji, Qiong Wu, Pingyi Fan · 5 authors
As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of offloading tasks to edge servers may expose vehicles to malicious external attacks, resulting in information loss or even tampering, thereby creating serious security vulnerabilities. Blockchain technology can maintain a shared ledger among servers. In the Raft consensus mechanism, as long as more than half of the nodes remain operational, the system will not collapse, effectively maintaining the system's robustness and security. To protect vehicle information, we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing. To address the additional latency introduced by blockchain, we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency, ensuring that the system meets the requirements for low latency and high reliability. Simulation results demonstrate that the optimized data extraction rate significantly reduces system delay, with relatively stable variations in latency. Moreover, the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments, such as 5G and next-generation smart city systems.
Shakil Ahmed, Muhammad Kamran Saeed, Ashfaq Khokhar
Quantum communication is poised to become a foundational element of next-generation networking, offering transformative capabilities in security, entanglement-based connectivity, and computational offloading. However, the classical OSI model-designed for deterministic and error-tolerant systems-cannot support quantum-specific phenomena such as coherence fragility, probabilistic entanglement, and the no-cloning theorem. This paper provides a comprehensive survey and proposes an architectural redesign of the OSI model for quantum networks in the context of 7G. We introduce a Quantum-Converged OSI stack by extending the classical model with Layer 0 (Quantum Substrate) and Layer 8 (Cognitive Intent), supporting entanglement, teleportation, and semantic orchestration via LLMs and QML. Each layer is redefined to incorporate quantum mechanisms such as enhanced MAC protocols, fidelity-aware routing, and twin-based applications. This survey consolidates over 150 research works from IEEE, ACM, MDPI, arXiv, and Web of Science (2018-2025), classifying them by OSI layer, enabling technologies such as QKD, QEC, PQC, and RIS, and use cases such as satellite QKD, UAV swarms, and quantum IoT. A taxonomy of cross-layer enablers-such as hybrid quantum-classical control, metadata-driven orchestration, and blockchain-integrated quantum trust-is provided, along with simulation tools including NetSquid, QuNetSim, and QuISP. We present several domain-specific applications, including quantum healthcare telemetry, entangled vehicular networks, and satellite mesh overlays. An evaluation framework is proposed based on entropy throughput, coherence latency, and entanglement fidelity. Key future directions include programmable quantum stacks, digital twins, and AI-defined QNet agents, laying the groundwork for a scalable, intelligent, and quantum-compliant OSI framework for 7G and beyond.
Dinh-Hieu Tran, Nazar Waheed, Yuris Mulya Saputra, Xingqin Lin · 15 authors
With the rapid development of technology, the number of smart mobile users is increasing, accompanied by growing demands from applications such as virtual/augmented reality (VR/XR), remote surgery, autonomous vehicles, and real-time holographic communications, all of which require high transmission rates and ultra-low latency in 6G and beyond networks (6G+). This poses enormous challenges in efficiently deploying large-scale networks, including network design, planning, troubleshooting, optimization, and maintenance, without affecting the user experience. Network Digital Twin (NDT) has emerged as a potential solution, enabling the creation of a virtual model that reflects the actual network, supporting the simulation of various network designs, applying diverse operating policies, and reproducing complex fault scenarios under real-world conditions. This motivate us for this study, where we provide a comprehensive survey of NDT in the context of 6G+, covering areas such as radio access networks (RAN), transport networks, 5G core networks and beyond (5GCORE+), cloud/edge computing, applications (blockchain, health system, manufacturing, security, and vehicular networks), non-terrestrial networks (NTNs), and quantum networks, from both academic and industrial perspectives. In particular, we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT. Then, we provide an extensive review of foundation technologies such as transport networks, cloud/edge computing, applications, NTNs, and quantum networks in NDT. Finally, we discuss the key challenges, open issues, and future research directions for NDT in the context of 6G+.
Nicola Giuseppe Marchioro, Yannis Velegrakis, Valentine Anantharaj, Ian Foster · 5 authors
Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata plays a key role in this context, capturing the origin, transformation, and usage of research artifacts. However, existing solutions often fall short when applied to distributed, multi-institutional settings. This paper introduces a modular, domain-agnostic architecture for provenance tracking in federated environments, leveraging permissioned blockchain infrastructure to guarantee integrity, immutability, and auditability. The system supports decentralized interaction, persistent identifiers for artifact traceability, and a provenance versioning model that preserves the history of updates. Designed to interoperate with diverse scientific domains, the architecture promotes transparency, accountability, and reproducibility across organizational boundaries. Ongoing work focuses on validating the system through a distributed prototype and exploring its performance in collaborative settings.
Blockchains support a rapidly growing digital economy. Through decentralization, they enable the ownership and transfer of digital assets without centralized intermediaries while resisting attacks, faults, and collusion. Yet decentralization is not guaranteed and must be sustained against external shocks. What design choices help blockchains maintain decentralization when disrupted? We find that resource flexibility, how easily consensus resources can be moved and redeployed, is key to sustained decentralization. Using three real-world shocks—China’s 2021 crypto mining ban, Hetzner’s 2022 shutdown of Solana validators, and Ethereum’s transition from proof-of-work to proof-of-stake—we show that blockchains whose consensus resources are more flexible recover decentralization faster. These findings offer actionable guidance. Blockchain designers should consider resource flexibility as a first-order design parameter. Operators should diversify infrastructure across jurisdictions to reduce vulnerability to localized shocks. Policymakers should be aware that regulations targeting blockchains can have unintended effects on decentralization.
We introduce EtherBee, a global dataset integrating detailed Ethereum node metrics, network traffic metadata, and honeypot interaction logs collected from ten geographically diverse vantage points over three months. By correlating node data with granular network sessions and security events, EtherBee provides unique insights into benign and malicious activity, node stability, and network-level threats in the Ethereum peer-to-peer network. A case study shows how client-based optimizations can unintentionally concentrate the network geographically, impacting resilience and censorship resistance. We publicly release EtherBee to promote further investigations into performance, reliability, and security in decentralized networks.
With the promise of greater decentralization and sustainability, Ethereum transitioned from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism. The new consensus protocol introduces novel vulnerabilities that warrant further investigation. The goal of this paper is to investigate the security of Ethereum's PoS system from an Internet routing perspective. To this end, this paper makes two contributions: First, we devise a novel framework for inferring the distribution of validators on the Internet without disturbing the real network. Second, we introduce a class of network-level attacks on Ethereum's PoS system that jointly exploit Internet routing vulnerabilities with the protocol's reward and penalty mechanisms. We describe two representative attacks: StakeBleed, where the attacker triggers an inactivity leak, halting block finality and causing financial losses for all validators; and KnockBlock, where the attacker increases her expected MEV gains by preventing targeted blocks from being included in the chain. We find that both attacks are practical and effective. An attacker executing StakeBleed can inflict losses of almost 300 ETH in just 2 hours by hijacking as few as 30 IP prefixes. An attacker implementing KnockBlock could increase their MEV expected gains by 44.5% while hijacking a single prefix for less than 2 minutes. Our paper serves as a call to action for validators to reinforce their Internet routing infrastructure and for the Ethereum P2P protocol to implement stronger mechanisms to conceal validator locations.
Existing blockchain sharding protocols have focused on eliminating imbalanced workload distributions. However, even with workload balance, disparities in processing capabilities can lead to differential stress among shards, resulting in transaction backlogs in certain shards. Therefore, achieving stress balance among shards in the dynamic and heterogeneous environment presents a significant challenge of blockchain sharding. In this paper, we propose ContribChain, a blockchain sharding protocol that can automatically be aware of node contributions to achieve stress balance. We calculate node contribution values based on the historical behavior to evaluate the performance and security of nodes. Furthermore, we propose node allocation algorithm NACV and account allocation algorithm P-Louvain, which both match shard performance with workload to achieve stress balance. Finally, we conduct extensive experiments to compare our work with state-of-the-art baselines based on real Ethereum transactions. The evaluation results show that P-Louvain reduces allocation execution time by 86% and the cross-shard transaction ratio by 7.5%. Meanwhile, ContribChain improves throughput by 35.8% and reduces the cross-shard transaction ratio by 16%.
Large Language Models (LLMs) have achieved remarkable success across a wide range of applications. However, individual LLMs often produce inconsistent, biased, or hallucinated outputs due to limitations in their training corpora and model architectures. Recently, collaborative frameworks such as the Multi-LLM Network (MultiLLMN) have been introduced, enabling multiple LLMs to interact and jointly respond to user queries. Nevertheless, MultiLLMN architectures raise critical concerns regarding the reliability and security of the generated content, particularly in open environments where malicious or compromised LLMs may be present. Moreover, reliance on centralized coordination undermines system efficiency and introduces single points of failure. In this paper, we propose a novel Trusted MultiLLMN framework, driven by a Weighted Byzantine Fault Tolerance (WBFT) blockchain consensus mechanism, to ensure the reliability, security, and efficiency of multi-LLM collaboration. In WBFT, voting weights are adaptively assigned to each LLM based on its response quality and trustworthiness, incentivizing reliable behavior, and reducing the impact of malicious nodes. Extensive simulations demonstrate that WBFT significantly improves both consensus security and efficiency compared to classical and modern consensus mechanisms, particularly under wireless network conditions. Furthermore, our evaluations reveal that Trusted MultiLLMN supported by WBFT can deliver higher-quality and more credible responses than both single LLMs and conventional MultiLLMNs, thereby providing a promising path toward building robust, decentralized AI collaboration networks.
Large Language Models (LLMs) demonstrate strong potential across a variety of tasks in communications and networking due to their advanced reasoning capabilities. However, because different LLMs have different model structures and are trained using distinct corpora and methods, they may offer varying optimization strategies for the same network issues. Moreover, the limitations of an individual LLM's training data, aggravated by the potential maliciousness of its hosting device, can result in responses with low confidence or even bias. To address these challenges, we propose a blockchain-enabled collaborative framework that connects multiple LLMs into a Trustworthy Multi-LLM Network (MultiLLMN). This architecture enables the cooperative evaluation and selection of the most reliable and high-quality responses to complex network optimization problems. Specifically, we begin by reviewing related work and highlighting the limitations of existing LLMs in collaboration and trust, emphasizing the need for trustworthiness in LLM-based systems. We then introduce the workflow and design of the proposed Trustworthy MultiLLMN framework. Given the severity of False Base Station (FBS) attacks in B5G and 6G communication systems and the difficulty of addressing such threats through traditional modeling techniques, we present FBS defense as a case study to empirically validate the effectiveness of our approach. Finally, we outline promising future research directions in this emerging area.
The Massive Internet of Things (MIoT) envisions an interconnected ecosystem of billions of devices, fundamentally transforming diverse sectors such as healthcare, smart cities, transportation, agriculture, and energy management. However, the vast scale of MIoT introduces significant challenges, including network scalability, efficient data management, energy conservation, and robust security mechanisms. This paper presents a thorough review of existing and emerging MIoT technologies designed to address these challenges, including Low-Power Wide-Area Networks (LPWAN), 5G/6G capabilities, edge and fog computing architectures, and hybrid access methodologies. We further investigate advanced strategies such as AI-driven resource allocation, federated learning for privacy-preserving analytics, and decentralized security frameworks using blockchain. Additionally, we analyze sustainable practices, emphasizing energy harvesting and integrating green technologies to reduce environmental impact. Through extensive comparative analysis, this study identifies critical innovations and architectural adaptations required to support efficient, resilient, and scalable MIoT deployments. Key insights include the role of network slicing and intelligent resource management for scalability, adaptive protocols for real-time data handling, and lightweight AI models suited to the constraints of MIoT devices. This research ultimately contributes to a deeper understanding of how MIoT systems can evolve to meet the growing demand for seamless, reliable connectivity while prioritizing sustainability, security, and performance across diverse applications. Our findings serve as a roadmap for future advancements, underscoring the potential of MIoT to support a globally interconnected, intelligent infrastructure.
Inter-provider agreements are central to 6G networks, where administrative domains must securely and dynamically share services. To address the dual need for transparency and confidentiality, we propose a privacy-enabled hybrid blockchain setup using Hyperledger Besu, integrating both public and private transaction workflows. The system enables decentralized service registration, selection, and SLA breach reporting through role-based smart contracts and privacy groups. We design and deploy a proof-of-concept implementation, evaluating performance using end-to-end latency as a key metric within privacy groups. Results show that public interactions maintain stable latency, while private transactions incur additional overhead due to off-chain coordination. The block production rate governed by IBFT 2.0 had limited impact on private transaction latency, due to encryption and peer synchronization. Lessons learned highlight design considerations for smart contract structure, validator management, and scalability patterns suitable for dynamic inter-domain collaboration. Our findings offer practical insights for deploying trustworthy agreement systems in 6G networks using privacy-enabled hybrid blockchains.
Alex Wong, Duncan McFarlane, Charlotte Ellarby, M.B. Lee · 5 authors
Twenty-five years ago, the specification of the Intelligent Product was established, envisaging real-time connectivity that not only enables products to gather accurate data about themselves but also allows them to assess and influence their own destiny. Early work by the Auto-ID project focused on creating a single, open-standard repository for storing and retrieving product information, laying a foundation for scalable connectivity. A decade later, the approach was revisited in light of low-cost RFID systems that promised a low-cost link between physical goods and networked information environments. Since then, advances in blockchain, Web3, and artificial intelligence have introduced unprecedented levels of resilience, consensus, and autonomy. By leveraging decentralised identity, blockchain-based product information and history, and intelligent AI-to-AI collaboration, this paper examines these developments and outlines a new specification for the Intelligent Product 3.0, illustrating how decentralised and AI-driven capabilities facilitate seamless interaction between physical AI and everyday products.
Mohammad R. Shakournia, Pooya Jamshidi, Hamid Reza Faragardi, Nasser Yazdani
Blockchain technology offers a decentralized and secure method for storing and authenticating data, rendering it well-suited for various applications such as digital currencies, supply chain management, and voting systems. However, the decentralized nature of blockchain also exposes it to vulnerabilities, particularly Byzantine faults, which arise when nodes in the network behave maliciously or encounter unexpected failures. Such incidents can result in inconsistencies within the blockchain and, in extreme scenarios, lead to a breakdown in consensus. Byzantine fault-tolerant consensus algorithms are crafted to tackle this challenge by ensuring that network nodes can agree on the blockchain's state even in the presence of faulty or malicious nodes. To bolster the system's resilience against these faults, it is imperative to detect them within the system. However, our examination of existing literature reveals a prevalent assumption: solutions typically operate under constraints regarding the number of faulty nodes. Such constraints confine the proposed solutions to ideal environments, limiting their practical applicability. In response, we propose a novel approach inspired by social paradigms, employing a trusted and fully monitored communication sub-process to detect Byzantine nodes. Upon detection, these nodes can be either disregarded in the consensus-building process, subjected to penalties, or undergo modifications as per the system's policy. Finally, we statistically demonstrate that our approach achieves a detection probability that exceeds 95\% for Byzantine nodes. In essence, our methodology ensures that if Byzantine nodes exhibit malicious behavior, healthy nodes can identify them with a confidence level of 95\%.
Yu Gao, Yu Zhang, Matija Piškorec, Claudio J. Tessone
Monero, a privacy-focused cryptocurrency, employs a decentralized peer-to-peer (P2P) network that plays a critical role in transaction propagation and consensus formation. While much research has explored Monero's privacy transaction mechanisms, its underlying P2P network architecture has remained relatively underexplored. In this study, building on our recent work on Monero network detection, we further investigate the network topology of Monero's P2P structure, which has evolved following recent protocol updates that enhanced security by obscuring peer information. Using k-core decomposition, we confirm that the Monero network exhibits a core-periphery structure, where a tightly interconnected core of supernodes is crucial for maintaining network cohesion, while peripheral nodes rely on these core nodes for connectivity. This structure explains why targeting central nodes does not easily lead to the rapid disintegration of the network's largest connected component while also providing a deeper understanding of the true architecture of Monero's peer protocol.
Muhammad Umar Farooq, Tanguy Cizain, Daniel Kaiser
The libp2p GossipSub protocol leverages a full-message mesh with a lower node degree and a more densely connected metadata-only (gossip) mesh. This combination allows an efficient dissemination of messages in unstructured peer-to-peer (P2P) networks. However, GossipSub needs to consider message size, which is crucial for the efficient operation of many applications, such as handling large Ethereum blocks. This paper proposes modifications to improve GossipSub's performance when transmitting large messages. We evaluate the proposed improvements using the shadow simulator. Our results show that the proposed improvements significantly enhance GossipSub's performance for large message transmissions in sizeable networks.
In a multi robot system establishing trust amongst untrusted robots from different organisations while preserving a robot's privacy is a challenge. Recently decentralized technologies such as smart contract and blockchain are being explored for applications in robotics. However, the limited transaction processing and high maintenance cost hinder the widespread adoption of such approaches. Moreover, blockchain transactions be they on public or private permissioned blockchain are publically readable which further fails to preserve the confidentiality of the robot's data and privacy of the robot. In this work, we propose RoboComm a Decentralized Identity based approach for privacy-preserving interaction between robots. With DID a component of Self-Sovereign Identity; robots can authenticate each other independently without relying on any third-party service. Verifiable Credentials enable private data associated with a robot to be stored within the robot's hardware, unlike existing blockchain based approaches where the data has to be on the blockchain. We improve throughput by allowing message exchange over state channels. Being a blockchain backed solution RoboComm provides a trustworthy system without relying on a single party. Moreover, we implement our proposed approach to demonstrate the feasibility of our solution.
Set reconciliation is a fundamental task in distributed systems, particularly in blockchain networks, where it enables synchronization of transaction pools among peers and facilitates block dissemination. Traditional set reconciliation schemes are either statistical, offering success probability as a function of communication overhead and symmetric difference size, or require parametrization and estimation of that size, which can be error-prone. We present CertainSync, a novel reconciliation framework that, to the best of our knowledge, is the first to guarantee successful set reconciliation without any parametrization or estimators. The framework is rateless and adapts to the unknown symmetric difference size. Reconciliation is guaranteed whenever the communication overhead reaches a lower bound derived from the symmetric difference size and universe size. Our framework builds on recent constructions of Invertible Bloom Lookup Tables (IBLTs), ensuring successful element listing as long as the number of elements is bounded. We provide a theoretical analysis proving the certainty of reconciliation for multiple constructions. Our approach is validated by simulations, showing the ability to synchronize sets with efficient communication costs while maintaining guarantees compared to baseline schemes. To further reduce overhead in large universes such as blockchain networks, CertainSync is extended with a universe reduction technique. We compare and validate this extension, UniverseReduceSync, against the basic framework using real Ethereum transaction hash data. Results show a trade-off between lower communication costs and maintaining guarantees, offering a comprehensive solution for diverse reconciliation scenarios.
Farhana Javed, Engin Zeydan, Josep Mangues‐Bafalluy, Kapal Dev · 5 authors
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, a type of 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.