The rapid growth of medical imaging data presents significant challenges in diagnostic accuracy, data privacy, and computational efficiency. Traditional centralized AI models struggle with scalability and pose risks to patient confidentiality due to data aggregation. Moreover, heterogeneous medical data across institutions complicates the development of robust diagnostic tools. To address these issues, we propose MID-LLM, a novel framework that integrates Large Language Models (LLMs) with a blockchain-based federated learning system for medical image analysis. It also ensures the security and privacy of sensitive medical data across decentralized networks. MID-LLM uses verification mechanisms to ensure the global model’s integrity. It also employs aggregation techniques to reduce bias and improve training efficiency. Experiments on the BraTS 2020 dataset show that MID-LLM outperforms traditional federated learning, achieving higher Dice scores with improved computational efficiency. These results highlight MID-LLM’s potential to enhance diagnostic accuracy while offering a scalable, secure solution for AI in healthcare.

The multilabel learning task aims to predict the associated multiple classes of a given example simultaneously. Such task becomes more challenging when data arrives in stream since it requires concept drift adaptative, robust, and fast algorithm. In this article, we present an online semi-supervised classification algorithm (OSMTS) for multilabel text streams. By leveraging a few labeled instances, OSMTS dynamically maintains the subspace of terms for each label with a set of evolving micro-clusters. For multilabel classification,$k$nearest micro-clusters are employed for prediction by using a nonparametric Dirichlet model. To handle the gradual concept drift in term space, the triangular time function is adopted to calculate the difference between term arriving time and cluster life span. Whereas, abrupt concept drift is dealt by considering two procedures: 1) deleting outdated micro-cluster by exploiting the exponential decay function and 2) creating new micro-clusters by adopting the Chinese restaurant process based on the Dirichlet process. The conducted experimental study provides a comparison with 12 state-of-the-art algorithms on nine datasets in terms of classification performance, runtime, and memory consumption.

Distributed data stream mining has gained increasing attention in recent years since many organizations collect tremendous amounts of streaming data from different locations. Existing studies mainly focus on learning evolving concepts on distributed data streams, while the privacy issue is little investigated. In this article, for the first time, we develop a federated learning framework for distributed concept-drifting data streams, called FedStream. The proposed method allows capturing the evolving concepts by dynamically maintaining a set of prototypes with error-driven representative learning. Meanwhile, a new metric-learning-based prototype transformation technique is introduced to preserve privacy among participating clients in the distributed data streams setting. Extensive experiments on both real-world and synthetic datasets have demonstrated the superiority of FedStream, and it even achieves competitive performance with state-of-the-art distributed learning methods.

In the context of streaming data, learning algorithms often need to confront several unique challenges, such as concept drift, label scarcity, and high dimensionality. Several concept drift-aware data stream learning algorithms have been proposed to tackle these issues over the past decades. However, most existing algorithms utilize a supervised learning framework and require all true class labels to update their models. Unfortunately, in the streaming environment, requiring all labels is unfeasible and not realistic in many real-world applications. Therefore, learning data streams with minimal labels is a more practical scenario. Considering the problem of the curse of dimensionality and label scarcity, in this article, we present a new semisupervised learning technique for streaming data. To cure the curse of dimensionality, we employ a denoising autoencoder to transform the high-dimensional feature space into a reduced, compact, and more informative feature representation. Furthermore, we use a cluster-and-label technique to reduce the dependency on true class labels. We employ a synchronization-based dynamic clustering technique to summarize the streaming data into a set of dynamic microclusters that are further used for classification. In addition, we employ a disagreement-based learning method to cope with concept drift. Extensive experiments performed on many real-world datasets demonstrate the superior performance of the proposed method compared to several state-of-the-art methods.

Due to the popularity of social media and online fora, such as Twitter, Reddit, Facebook, and Wechat, short text stream clustering has gained significant attention in recent years. However, most existing short text stream clustering approaches usually work on static data and tend to cause a "term ambiguity" problem due to the sparse word representation. Beyond, they often exploit short text streams in a batch way and are difficult to find evolving topics in term-changing subspaces. In this article, we propose an online semantic-enhanced graphical model for evolving short text stream clustering (OSGM), by exploiting the word-occurrence semantic information and dynamically maintaining evolving active topics in term-changing subspaces in an online way. Compared to the existing approaches, our online model is not only free of determining the optimal batch size but also lends itself to handling large-scale data streams efficiently. It is also able to handle the "term ambiguity" problem without incorporating features from external resources. More importantly, to the best of our knowledge, it is the first work to extract evolving topics in term-changing subspaces automatically in an online way. Extensive experiments demonstrate that the proposed model yields better performance compared to many state-of-the-art algorithms on both synthetic and real-world datasets.

Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution.Existing approaches often exploit short text streams in a batch way.However, determine the optimal batch size is usually a difficult task since we have no prior knowledge when the topics evolve.In addition, traditional independent word representation in the graphical model tends to cause "term ambiguity" problem in short text clustering.Therefore, in this paper, we propose an Online Semantic-enhanced Dirichlet Model for short text stream clustering, called OSDM, which integrates the word-occurrence semantic information (i.e., context) into a new graphical model and clusters for each arriving short text automatically in an online way.Extensive results have demonstrated that OSDM gives better performance compared to many state-ofthe-art algorithms on both synthetic and realworld data sets.