As 6G communication technology advances, there is a growing trend of incorporating blockchain technology, which has already demonstrated its effectiveness in multiple areas. Merging blockchain technology with 6G communication opens up novel prospects for consumer electronics, facilitating the creation of secure, private, and decentralized networks, along with pioneering applications and services. We explore the synergistic incorporation of blockchain with 6G communication networks to enable secure and decentralized connectivity tailored for consumer electronics. To this end, a multi-party, dependable framework comprising intelligent edge servers, blockchain consensus, and resource-constrained electronic devices is proposed. Analytical models characterize the system’s unique cost and incentive tradeoffs, accounting for factors like energy, latency, credibility, and capacity. We analyze symmetric and asymmetric information scenarios, providing insights into optimal resource allocation strategies in different knowledge conditions within the network. Extensive simulations validate gains over benchmarks across mobile augmented reality gaming and distributed machine learning workloads, achieving over 90% offloading efficiency within 50ms latency targets as infrastructure scales up to 100 edge servers and 2000 devices. These results establish the feasibility of blended edge intelligence, cryptography, and wireless advancements in realizing next-generation consumer solutions spanning metaverse, ambient computing, and industrial IoT while preserving user control.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

Researchers have suggested leveraging label correlation to deal with the exponentially sized output space of label distribution learning (LDL). Among them, some have proposed to exploit local label correlation. They first partition the training set into different groups and then exploit local label correlation on each one. However, these works usually apply clustering algorithms, such as $K$ -means, to split the training set and obtain the clustering results independent of label correlation. The structures (e.g., low rank and manifold) learned on such clusters may not efficiently capture label correlation. To solve this problem, we put forward a novel LDL method called LDL by partitioning label distribution manifold (LDL-PLDM). First, it jointly bipartitions the training set and learns the label distribution manifold to model label correlation. Second, it recurses until the reconstruction error of learning the label distribution manifold cannot be reduced. LDL-PLDM achieves label-correlation-related partition results, on which the learned label distribution manifold can better capture label correlation. We conduct extensive experiments to justify that LDL-PLDM statistically outperforms state-of-the-art LDL methods.

In the realm of consumer electronics for 6G communication, AI has emerged as a significant player. However, the proliferation of devices at the edge of network causes the generation of extensive multimodal data, encompassing user behavior records, audio, and video. The influx of data poses fresh challenges concerning security and privacy. Consequently, there has been a surge in research and the implementation of AI-driven methods to protect privacy in response to these challenges. A differential privacy federated learning framework with adaptive clipping, which uses Gaussian mechanism, is proposed to mitigate privacy issue. Simultaneously, conventional federated learning depends on a centralized server and is susceptible to single points of failure and malicious node attacks. The explicit transmission of intermediate parameters can lead to the inference of private data. Therefore, a federated learning model based on blockchain is proposed to enhance decentralization, security, and fairness. Results demonstrate that the proposed framework achieves more accurate results than centralized federated learning, decentralized wireless federated learning, fused real-time sequential deep extreme learning machine, and federated learning combined with blockchain and local differential privacy, increasing the classification accuracy by 13.25%, reducing the training loss, training time, and communication overhead by 28.36%, 51.73%, and 61.44% respectively.