As data sharing on the Internet of Medical Things (IoMT) become more complicated, the problems of divergent interests, unregulated policies, privacy and security, and the resource constraints of data owners have drawn the attention of researchers. To address the problems, this paper provides resource management in the IoMT using a proposed edge-empowered blockchain federated learning system. Also, an improved linear regressor model is proposed as the global learning model for the federated learning system. Gradient parameters are encrypted using Paillier encryption on the federated server side before they are shared by the federated clients. Blockchain is deployed to provide new security features for IoMT and edge computing. Moreover, all transactions of IoMT and edge devices are stored on the blockchain for secure cataloguing and auditing. Edge computing is employed to handle complex computing tasks on behalf of IoMT devices. Extensive simulations are conducted to validate the efficacy of the proposed system model. The results show that computing costs are minimized while still achieving the benefits of security and privacy in the proposed system. Furthermore, security analysis shows that the proposed system is protected from security attacks.

We propose a decentralized medical trust management system using blockchain-based federated learning for large-scale Internet of Things (IoT) systems. The proposed system enables health institutions to share data without revealing the privacy of data owners. Health institutions form coalitions and the leader of each coalition is elected based on the proposed proof-of-trust collaboration (PoTC) consensus protocol. The PoTC consensus protocol is based on a weight difference game where trust scores, trust consistency value, and trust deviation are factors used for evaluating nodes in the blockchain. The trust of a node is obtained either through direct trust or recommended trust evaluations. Each leader elects an aggregator who has the most credibility to manage the proposed federated learning system. The leaders become the federated clients as well as validators while the aggregator is the federated server. To ensure the decentralization of nodes, a consortium blockchain is employed. Extensive simulations are performed, which show that the proposed system not only demonstrates scalability and credibility without compromising the accuracy, convergence, and resilience properties against malicious attackers but also outperforms existing trust management systems. A security analysis is also conducted, which shows that the proposed system is robust against trust-related attacks.

In real-world Industrial Internet of Things (IIoT) scenarios, due to the limited storage capacity of IIoT devices, fresh data continuously received by diverse devices will overwrite the outdated data and change the local data distribution. However, state-of-the-art studies have demonstrated that federated learning tends to focus on training with fresh data, and the latest global model may forget the historical update directions (i.e., catastrophic forgetting). This issue can significantly degrade the global model accuracy. Existing methods primarily focus on integrating outdated data characteristics into fresh data but overlook the large parameter update gap between global and local models during global aggregation. This gap can cause the global model updates to deviate from the optimal direction. To this end, we propose a federated adaptive weighted aggregation method based on model consistency (FedAWAC). Specifically, FedAWAC measures the model consistency on devices and dynamically adjusts the aggregation weights of each local model, thereby guiding the global model toward optimal updates. Furthermore, FedAWAC integrates $\mathcal {M}$ historical global models most correlated to the latest global model on the cloud server to overcome catastrophic forgetting. Experiments on four different datasets (nonidentically and independently distributed settings) indicate that compared to five baselines, FedAWAC can improve global model accuracy by an average of 1.86%, reduce the forgetting rate by an average of 3.91%, and save average memory usage by up to 2.57 GB.

Background: Proper biodiversity conservation and strategies for sustainability in environmental and public health are essential measures for addressing the problems of water-related and zoonotic-caused pandemics. It is generally assumed that a resurgence in an epidemic disease is directly linked to negligence in environmental protection policies, but there are sparse scientific publications supporting the claim. Methods: To address this issue, we collected data on the global pre- and post- industrialization scenarios and subjected it to multivariate analyses to investigate the relationship between the lack of proper environmental protection and the emergence of new diseases. Result: Our investigations found a statistically significant association between the loss of wildlife habitat and the emergence of novel diseases. The study also revealed that wildlife-related zoonotic disorders caused more than 220 million deaths amongst global pandemics. More than 30 million deaths were attributed to waterborne diseases due to improper waste management and wastewater treatment. Thus, it is recommended that other environmental parameters (e.g., pollution phase) need to be investigated to fully understand the complex relationship between environmental protection and emergence of new zoonotic diseases.