Federated Learning has emerged as a promising paradigm for collaborative model training in healthcare. FL allows institutions to share knowledge without compromising patient privacy. However, data annotation remains a bottleneck, especially in medical image studies. This work proposes a Federated Active Learning with a Transfer Learning framework for efficient labeling in lung cancer diagnosis. Using ensemble entropy-based uncertainty assessment, FAL-TL streamlines sample annotation, optimizing training across distributed healthcare institutions while safeguarding patient privacy. Using the IQOTH/NCCD Lung Cancer and Chest CT-Scan images Dataset, our FAL-TL framework achieves an impressive 99.20% accuracy, surpassing traditional machine learning models. By integrating transfer learning, FAL-TL adapts pre-trained models to healthcare datasets, significantly enhancing diagnostic accuracy. This research contributes to advancing FL techniques in healthcare, offering a scalable and privacy-preserving solution with transformative implications for diagnostics and patient care.

The scientific community has recently focused on intelligent models for predicting and optimizing EV energy management. Despite numerous studies in energy management optimization, there’s a critical need to address the trade-off between energy consumption and occupant comfort. Existing IoT systems face challenges in data analytics security and authenticity, highlighting the need for contemporary models to overcome data privacy and cost-related issues. This study introduces a smart contract model based on optimization and control modules, aiming to manage energy consumption while satisfying user comfort requirements intelligently. Introducing a smart contract model with hierarchical layers—prediction, optimization, control, and Blockchain—the proposed approach intelligently manages energy consumption while meeting user comfort requirements. Utilizing a Kalman filter for prediction and the BAT algorithm for optimization, the model integrates modules to tailor user preferences and enhance comfort. The synergy between the optimization module and a convolutional FLC enhances system performance, ensuring minimized energy usage and elevated user comfort levels. The study also evaluates the model’s implementation of the Hyperledger Fabric network, assessing outcomes regarding caliper, latency, throughput, and resource utilization.

With the vast development of Internet-of-Things (IoT) ecosystem, various types of information, such as healthcare records and physical resources, are integrated for different types of applications. Due to the sheer number of connected IoT devices, which generate a large amount of data, Distributed Ledger Technology, such as Blockchain and IOTA have been recently applied in developing access control models, yet they involve significant energy due to mining, low throughput, non-scalable, and computational overhead that is not acceptable for IoT resource-constrained devices. In this paper, we propose a Scalable Decentralized and Lightweight Access Control framework (SDAC) by using the IOTA platform. IOTA is an emerging distributed ledger technology that has significant features for IoT, such as zero fees transactions, scalability, security and energy efficiency. The proposed SDAC aims to improve security, authorize, and authenticate users when accessing data by using the IOTA Masked Authenticated Messaging (MAM) protocol. MAM ensures access control by encrypting and granting permission to only authorized users. The experimental results indicate that IOTA MAM is a feasible solution that can be used for managing authorization in the IoT domain.

In the Internet of things (IoT), security and privacy issues are a fundamental challenge determining the successful implementation of many IoT applications. Distributed ledger technology (e.g., Blockchain) offers a great promise to solve these issues. Blockchain-based solutions support security and privacy, yet they involve significant energy due to mining, low throughput, and computational overhead that is not acceptable for IoT resource-constrained devices. In this paper, we propose an energy-efficient Privacy-Preserving Distributed Intelligence approach (PPDI) by adopting the IOTA technology. IOTA is an emerging distributed ledger technology that allows for zero fees transactions for the IoT. The proposed PPDI aims to address the privacy issues in the IoT by using the IOTA Masked Authenticated Messaging (MAM) protocol. MAM ensures privacy by encrypting and granting permission to authorized users to access data. This paper presents a healthcare scenario that demonstrate how IOTA MAM can be used to address the privacy issue in the IoT. The experimental results clearly show that IOTA MAM is a feasible solution that can be used to solve privacy related issues in the IoT domain.

It is estimated that there will be approximately 26 to 30 billion Internet of Things (IoT) devices connected to the Internet by 2020. This presents research challenges in areas such as data processing, infrastructure scalability, and privacy. Several studies have demonstrated the benefits of using distributed intelligence to overcome these challenges. This article reviews existing state-of-the-art distributed intelligence approaches in IoT and focuses on the motivations and challenges for distributed intelligence in IoT. We propose a potential solution based on IOTA (Tangle), a platform that enables highly scalable transaction-based data exchange amongst large quantities of smart things in a peer-to-peer manner, together with mobile agents to support distributed intelligence. Challenges and future research directions are also discussed.