Multi-view learning aims to integrate multi-source information for a comprehensive data representation, which has gained widespread attention in image processing. Each view contains view-specific noise and joint features associated with other views, and thus exploring the specificity and consistency among views is a typical solution to deal with multi-view data for learning discriminative representations. In this paper, we present a theory-induced model, termed Adversarial Distribution Alignment Network (ADAN), which learns view-invariant features and alleviate the negative impact of view-specific noise. We first demonstrate the necessity of suppressing view-specific noise and capturing view-invariant features inspired by the theory of view generalization, and then derive two collaborative modules: a feature disentangler and an adversarial alignment module. In detail, the feature disentanglement separates view-specific noise and view-invariant features by minimizing the mutual information between them. Following this, a negative entropy is proposed to suppress the negative impact of view-specific noise. Meanwhile, the adversarial module uses the adversarial technique that can fit more complex data conformed to different distributions to adaptively align cross-view features so that features encoded in different views converge. Substantial experiments are constructed on multi-view datasets, demonstrating that ADAN can achieve more promising performance compared to other superior methods. Code is available at https://github.com/huangsuj/ADANet.

Brain tumors account for approximately 2.5% of cancer‐related deaths. Accurate classification of brain tumor types is essential for timely diagnosis and enhancing survival rates. Convolutional neural networks (CNNs) have demonstrated state‐of‐the‐art performance in computer‐aided diagnosis of brain tumors; however, the quality and availability of medical data significantly influence this process. Medical data must adhere to stringent privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Federated learning (FL) enables the sharing of only model update parameters during collaborative training on locally stored data. However, these parameters may inadvertently enable reconstruction of the original data. Furthermore, medical data often exhibit nonindependent and nonidentically distributed (non‐IID) characteristics, impeding model training performance. To address these challenges, this paper proposes a scheme that partitions confidential data into multiple segments during FL training, ensuring that only a subset exceeding a predefined threshold can reconstruct the data. The proposed scheme guarantees enhanced security, distributed control, and fault tolerance. In addition, this paper introduces a Conditional Mutual Information (CMI) regularizer to mitigate variability in model predictions. By minimizing the Kullback–Leibler (KL) divergence between local and global feature distributions, the CMI regularizer substantially enhances performance and convergence stability. Extensive experiments conducted on the Figshare dataset with varying α‐values for data distributions validate the efficacy of the proposed model. Compared to FedAvg, FedProx, and FedDyn at α = 0.3, as well as the central model, the proposed model achieves a top‐1 accuracy of 92.94% on the Figshare dataset, surpassing FedProx, FedAvg, and FedDyn by 2.42%, 2.82%, and 3.53%, respectively. Federated IID achieves performance comparable to that of the central model, further demonstrating its viability for practical applications.

Developing effective platforms for economic energy management is considered a pivotal issue in the field of electric vehicles (EVs). To implement a cost-effective energy management platform (EMP), developers must overcome two major challenges. The first challenge lies in the environmental dynamic nature, such as EV location, energy price fluctuations, storage levels, and parking availability at charging stations. This causes most traditional one-shot optimizations to fail. The second challenge pertains to the lack of regulation in EV energy exchanges. To address these challenges, we propose a cost-aware two-stage EMP based on blockchain and deep reinforcement learning (DRL), namely, TEMP. Specifically, TEMP first develops a sharding-based blockchain energy management framework, which guarantees trust, security, privacy, traceability, and accountability without the need for intermediaries. Then, considering the complex and high-dimensional environment, TEMP devises a two-stage cooperative scheduling scheme by combining ant colony optimization (ACO) with proximal policy optimization (PPO) to enhance learning effectiveness. Evaluations show that TEMP outperforms the two state-of-the-art baselines by 12.3% and 4.4% in terms of long-term profits while reducing costs by 6.7% and 2.8%, respectively. Moreover, energy transaction efficiency can be ensured when the EV number of blockchain networks is gradually increased.

Federated learning is increasingly attractive, however as the number of training samples on a single device is too small and the training tasks of the devices are different, it faces the few-shot multitask learning problem. Moreover, federated learning frameworks are usually vulnerable to malicious attacks of the central server and diverse clients. To address these problems, we propose a decentralized federated meta-learning framework (DFMLF) for few-shot multitask learning. In DFMLF, the devices take the rapid adaptation as objective and learn the meta-knowledge shared by tasks to deal with the few-shot multitask problem. In addition, DFMLF conducts cross-validation and secure aggregation mechanism by a small number of committee nodes, which not only eliminates the central server to avoid the security risks brought by the malicious central server, but also avoids the attack of malicious devices. Moreover, to address the extra communication cost brought by the committee strategy, we propose a communication-efficient method to make the training and aggregation carried out in parallel. We conduct extensive experiments based on real-world data sets, and the experimental results demonstrate the effectiveness, robustness, and efficiency of our framework.

With a growing number of alternative Internet of Things (IoT) services that provide the same functionalities, Quality-of-Service (QoS) prediction has become an important research issue. Conventional central methods require that the historical QoS data is centralized. While the QoS data may be distributed in different edge servers. Due to privacy, these edge servers may not be willing to share their data with others for a better model representation. Besides, as each edge server is in charge of the collecting QoS data from the users in a specific area, the QoS data are likely to be heterogeneous. In this article, we propose a personalized federated tensor factorization framework for distributed privacy-preserving IoT services QoS prediction. We first adopt tensors to represent the QoS data with multidimensions and initialize a personalized model for each edge server. Then, each edge server performs local tensor factorization and exchanges the public component with a parameter master. By constraining the consistency of the global public component and the local personalized public components, the global model can learn information from edge servers during the training process. In addition, we conduct extensive experiments on a real-world QoS data set, the experimental results demonstrate that the proposed framework is efficient and effective.

Mobile devices can generate a tremendous amount of unique data, and thus, create countless opportunities for deep learning tasks. Due to the concerns of data privacy, it is often impractical to log all the data to a central server for training a satisfactory model. In federated learning, the participating devices can train a shared global model collaboratively while keeping their data locally. However, it is not a trivial task to train the deep neural networks (DNNs) with millions and billions of parameters on resource-constrained mobile devices in a federated manner. We replace each fully connected (FC) layer with two low-rank projection matrices to compact the DNNs model, and establish a global error function to recover the outputs of the compressed DNNs model. Then, we design a communication-efficient federated optimation Algorithm to reduce communication cost further. Considering that the heterogeneous devices may run different models at the same time, we devise three different training patterns to integrate the heterogeneous devices running different models. We conduct extensive experiments on both independently identically distribution (IID) and non-IID data sets. The experimental results demonstrate that the proposed framework can significantly reduce the number of parameters and communication cost while maintaining performance.

Federated learning allows multiple participants to collaboratively train an efficient model without exposing data privacy. However, this distributed machine learning training method is prone to attacks from Byzantine clients, which interfere with the training of the global model by modifying the model or uploading the false gradient. In this article, we propose a novel serverless federated learning frameworkCommittee Mechanism based Federated Learning(CMFL), which can ensure the robustness of the algorithm with convergence guarantee. In CMFL, a committee system is set up to screen the uploaded local gradients. The committee system selects the local gradients rated by the elected members for the aggregation procedure through the selection strategy, and replaces the committee member through the election strategy. Based on the different considerations of model performance and defense, two opposite selection strategies are designed for the sake of both accuracy and robustness. Extensive experiments illustrate that CMFL achieves faster convergence and better accuracy than the typical Federated Learning, in the meanwhile obtaining better robustness than the traditional Byzantine-tolerant algorithms, in the manner of a decentralized approach. In addition, we theoretically analyze and prove the convergence of CMFL under different election and selection strategies, which coincides with the experimental results.

With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions.

With the popularity of cloud computing, the use of services provided by the cloud is increasing. Due to the high communication cost, and data privacy issues, a new crosscloud collaborative computing model is demanded instead of a single giant cloud. Coping with federated learning and Joint-Cloud, we propose a federated learning-based collaborative learning framework, in which the distributed cloud entities are able to learn the same model collaboratively. As compared to traditional cloud-centric approaches, the framework for JointCloud can reduce the network bandwidth overhead and guarantee privacy. However, there are two crucial challenges in the federated manner: heterogeneity and high communication overhead. To address the heterogeneity, we propose a Teacher-Student mechanism, the key of which is a regularization term incorporated with the objective function so as to adjust the gradients from the clients among JointCloud with different data distribution. Then, based on the Teacher-Student mechanism, we further present a communication-efficient federated optimation approach via joint Identification-Verification to reduce the communication rounds. We conduct extensive experiments on Non-IID datasets. The experimental results demonstrate that the proposed framework can significantly reduce communication costs and improve performance.