The development of Large Language Models (LLMs) faces a significant challenge: the exhaustion of publicly available fresh data. This is because training an LLM requires a large demand for new data. Federated learning emerges as a promising solution, enabling collaborative model to contribute their private data to LLM global model. However, integrating federated learning with LLMs introduces new challenges, including the lack of transparency and the need for effective unlearning mechanisms. Transparency is essential to ensuring trust and fairness among participants, while accountability is crucial for deterring malicious behaviour and enabling corrective actions when necessary. To address these challenges, we propose a novel blockchain-based federated learning framework for LLMs that enhances transparency, accountability, and unlearning capabilities. Our framework leverages blockchain technology to create a tamper-proof record of each model's contributions and introduces an innovative unlearning function that seamlessly integrates with the federated learning mechanism. We investigate the impact of Low-Rank Adaptation (LoRA) hyperparameters on unlearning performance and integrate Hyperledger Fabric to ensure the security, transparency, and verifiability of the unlearning process. Through comprehensive experiments and analysis, we showcase the effectiveness of our proposed framework in achieving highly effective unlearning in LLMs trained using federated learning. Our findings highlight the feasibility of integrating blockchain technology into federated learning frameworks for LLM.

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack verifiable mechanisms, leading to challenges in establishing trust. This paper delves into the innovative integration of blockchain technology with federated learning to surmount these obstacles. Blockchain fortifies the unlearning process through its inherent qualities of immutability, transparency, and robust security. It facilitates verifiable certification, harmonizes security with privacy, and sustains system efficiency. We introduce a framework that melds blockchain with federated learning, thereby ensuring an immutable record of unlearning requests and actions. This strategy not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Our key contributions encompass a certification mechanism for the unlearning process, the enhancement of data security and privacy, and the optimization of data management to ensure system responsiveness in IoT scenarios.

The rapid growth and escalating complexity of the Internet of Things (IoT) necessitate meticulous attention to ensure efficient and secure transactions among various autonomous components. To address this critical issue, this study proposes the integration of multiagent systems (MASs) and blockchain technology within the IoT domain. Uniquely, our approach employs smart contracts to manage exchanges between autonomous entities, thereby offering enhanced security, transparency, and reliability. The study introduces a set of innovative algorithms that regulate agent activities, such as creating blocks, sharing information, and conducting rating processes. Additionally, it provides a detailed analysis of their privacy and security aspects. Compared to traditional multiagent frameworks, empirical evidence demonstrates significant improvements in efficiency, adaptability, and scalability. This scholarly effort lays a robust foundation for further investigations into applying blockchain to enhance MASs, potentially paving the way for more sophisticated and context-specific strategies across various IoT fields.

Large language models (LLMs) have transformed the way computers understand and process human language, but using them effectively across different organizations remains still difficult. When organizations work together to improve LLMs, they face several main challenges. First, organizations hesitate to share their valuable data with others. Second, competition between organizations creates trust problems during collaboration. Third, new privacy laws require organizations to be able to delete specific data when requested, which is especially difficult when multiple organizations are learning from shared data. Traditional federated learning approaches do not address these interconnected challenges, particularly in scenarios where participants cannot fully trust each other or the central aggregator. To overcome these limitations, we propose a hybrid blockchain-based federated learning framework that uniquely combines public and private blockchain architectures with multi-agent reinforcement learning. Our framework enables transparent sharing of model update through the public blockchain while protecting sensitive computations in private chains. Each organization operates as an intelligent agent, using Q-learning to optimize its participation strategy and resource allocation, thus aligning individual incentives with collective goals. Notably, we introduce an efficient unlearning mechanism based on Low-Rank Adaptation (LoRA) that enables selective removal of specific data contributions without compromising the model's overall performance. Through extensive experimentation on real-world datasets, we demonstrate that our framework effectively balances privacy protection, trust establishment, and regulatory compliance while maintaining high model performance.

Federated learning (FL) FL has emerged as a promising privacy-preserving machine-learning technology, enabling multiple clients to collaboratively train a global model without sharing raw data. With the increasing adoption of FL in Internet of Things (IoT) scenarios, concerns about security and privacy have become critical. In particular, gradient inversion attacks and poisoning attacks pose significant threats to the integrity and effectiveness of the global model. In response, we propose a comprehensive blockchain-based defense mechanism that effectively protects FL systems from such attacks. We develop a novel combination of techniques, including public blockchain level protection and private blockchain level protection, which work in tandem to prevent attackers from reconstructing figures using the obtained gradients. This unique combination of methods provides a robust defense against gradient inversion attacks in FL IoT scenarios. We conduct extensive experiments to validate the effectiveness of our proposed approach against gradient inversion and poisoning attacks. Our results demonstrate improved accuracy and stable convergence of training loss under poisoning attacks, indicating that our method can be applied to a wide range of FL IoT scenarios, enhancing both the security and privacy of distributed machine-learning systems.

Implementing federated learning within the Internet of Everything (IoE) framework poses substantial computational challenges, stemming from extensive client involvement, which can lead to escalated training expenses and diminished convergence rates. While many studies have investigated the combination of federated learning and blockchain networks, the integration of public and private chains to enhance federated learning performance remains largely unexplored. In this study, we introduce an innovative methodology that unifies public and private chains to mitigate clients’ computational demands while preserving data privacy and security, demonstrating compatibility within the IoE milieu and yielding favorable outcomes. To facilitate secure model migration and expedite training without incurring excessive computation costs, we delineate a blockchain-anchored model migration scheme tailored for resource-limited IoT infrastructures, establishing a private chain mechanism to incentivize companies possessing multiple devices or clients to prioritize model training. Employing blockchain technology guarantees trustworthiness in model migration, precluding the disclosure of devices’ confidential data. Overall, our innovative method provides an effective solution that improves the accuracy, privacy, and security of federated learning while reducing clients’ computational burdens within the context of the Internet of Everything (IoE).

As a result of the rapid development of Internet of Things (IoT) systems, an increasing number of academics are focusing on finding new applications for IoT systems. For IoT systems, crowdsourcing is a prevalent practise. Due to the large number of deployed devices in IoT networks, more research is still required on the privacy and trust issues that arise when utilizing crowdsourcing. As a result of the characteristics of social computing, the crowdsourcing network poses issues in terms of confidentiality and reliability. To consolidate and create this industry, we have built a differentially private crowdsourcing system that integrates public and private blockchains to address the privacy and trust issues of conventional crowdsourcing systems. Our proposed solution enables varying levels of privacy protection to protect the user's identity and location. Moreover, the installation of blockchain networks might potentially ensure the data's integrity. In the conclusion of this article, the possibility of deploying a crowdsourcing system with blockchain in IoE networks is examined.

Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The learning speed and efficiency in reinforcement learning can be improved by sharing the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by the requirement that during advising all learning agents in a reinforcement learning environment must have exactly the same actions. To address the above limitation, this article proposes a novel differentially private advising framework for multiagent reinforcement learning. Our proposed approach can significantly improve the application of conventional advising frameworks when agents have one different action. The approach can also widen the applicable field of advising and speed up reinforcement learning by triggering more potential advising processes among agents with different actions.

Although the blockchain was introduced in 2008 by Nakamoto, it has developed into a hot topic because of its decentralized characteristics. The evolution of the wireless network is moving from 4G to 5G and 6G networks. So the research on the combination of blockchain and 5G and 6G networks should be carried out as soon as possible. In this chapter, we have presented a detailed survey of the blockchainenabled technologies, applications and services in 5G and 6G networks. Moreover, the challenges and solutions, such as security -related, privacy -related and other related, of deploying blockchain in 5G and 6G networks, are also proposed. We hope that this discussion will stimulate interest and further research on implementing blockchain in future 5G and 6G networks.