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to the limited storage capacity of IIoT devices, fresh data continuously received by diverse devices will overwrite the outdated data and change the local data distribution.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 Existing methods primarily focus on integrating outdated data characteristics into fresh data but overlook the large parameter update gap between global and local models during global aggregation.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.

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.