Federal learning provides a secure platform for sharing sensitive data, yet imposes stringent requirements on the data. Non-IID data often cannot fully enjoy the convenience it offers. When clients possess divergent feature sets, retaining only the common features is a prevalent yet suboptimal practice. This paper proposes a novel omnidirectional federated learning framework that employs a Star collaboration network designed to leverage independent information from client nodes for feature reconstruction of other clients. It establishes an approximate distribution network, reinforcing feature correlations while overcoming data isolation seen in traditional federal learning. Additionally, homomorphic encryption is utilized to ensure data security throughout the transmission process. Experimental evaluations on structured datasets demonstrate that the reconstructed prediction results closely approximate those under the condition of complete data, confirming the effectiveness of the Star network in data completion and multi-party prediction scenarios.

Anomaly detection in surveillance videos aims to differentiate anomalies from regular events by discriminative representations, which has gathered considerable attention due to its significant effect to public security. However, most existing works are limited in the lack of annotated samples, and lots of approaches find it challenging to avoid the well-reconstruction of anomalous data. To alleviate these issues, we propose a dual distillation fusion framework for weakly supervised anomaly detection. We reformulate the anomaly detection problem into two steps, namely filtering anomalies and inpainting normal patterns. Each step corresponds to one branch of the dual distillation. Specifically, the dual distillation comprises the contrastive distillation module and the inpainting distillation module. The contrastive distillation optimizes the encoder to filter out abnormal features and capture key normal features, while the inpainting distillation refines the decoder to inpaint normal patterns on the encoded features. The contrastive distillation module and the inpainting distillation module are optimized iteratively in a self-training manner with video-level labeled data. Moreover, a joint optimization module is devised to effectively fuse the distilled encoder and decoder, thereby collectively improving the anomaly detection performance. During the training phase, we take into account the diversity of normal samples by selecting pseudo normal and abnormal samples with high confidence from abnormal videos. These selected samples, along with original normal frames, are then fed into the subsequent training iterations to enhance the distinguishing ability of the model. Experimental results show that our proposed method performs competitively on five benchmark datasets.

Novel benchmarks for multiobjective neural archi-tecture search are emerging consistently. It stimulates the need of knowledge transfer techniques to facilitate the repetitive and cumbersome search process. One-shot surrogate is hereby proposed to transfer knowledge from source problems. Specif-ically, a Pareto-aware super-surrogate construction technique is proposed aiming at efficient exploitation of knowledge from benchmarks to make transfer easier. Then, during the surrogate prediction process, appropriate sub-surrogates are sampled from the super-surrogate to jointly guide the evaluation decision. The multiobjective surrogate is also redesigned as objective-wise, decision-making, and combinative ones, so that it can make transfer flexible, as well as evade from the scalarization issue in performance measurement. This knowledge transfer scheme assists the convergence of the search process using previously constructed surrogates from multiple source problems, balancing exploration and exploitation.

The exponential growth of the Internet of Things (IoT), driven by the increasing number of connected devices and sensors, is profoundly transforming many industries. This proliferation has been enabled by advances in storage and processing capabilities, facilitating the implementation of deep model. However, concerns surrounding privacy and substantial bandwidth required for data transmission arise from the centralized processing of data collected from numerous distributed devices. To address these issues, federated learning (FL) has emerged as a viable solution with a decentralized strategy. It ensures data privacy by allowing devices to locally train models while keeping their data localized, thus reducing centralization needs and concerns related to privacy and bandwidth. To mitigate the data heterogeneity problem in FL scenarios, we propose federated pattern extraction clustering (FedPEC) to cluster clients with similar data distributions. Compare to existing client clustering methods, FedPEC requires no additional data transmission and ensures great flexibility, scalability and privacy. Furthermore, we discuss the discrepancy between representation and data distribution in existing methods from the perspective of pattern representation. Based on this, we propose a variant of FedPEC as a solution, which has achieved excellent performance on multiple FL data sets.

Multiparty learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multiparty learning approaches are confronted with obstacles, such as system heterogeneity, statistical heterogeneity, and incentive design. Determining how to deal with these challenges and further improve the efficiency and performance of multiparty learning has become an urgent problem to be solved. In this article, we propose a novel contrastive multiparty learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication and analogize multiparty learning as a many-to-one knowledge-sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets.

The powerful deep learning models in remote sensing are inseparable from the support of massive data. However, the privacy and sensitivity of remote sensing data (RSD) restrict the possibility of each party to collaboratively train and share a large general model. Although multi-party learning (MPL) is a feasible solution, it is difficult for the existing MPL methods to uniformly process different remote sensing tasks (RSTs), and the data held by each party is non-independent and identically distributed, heterogeneous and multi-sources. Therefore, it is urgent to explore a solution for the personalized processing of different RSTs. In this paper, we formulate a novel multi-party personalized collaborative learning (MPCL) framework in terms of models and tasks. Specifically, in each iteration of the communication round, we aim to decouple personalized model optimization from global model learning. Different participants are allowed to explore their personalized local models at a certain distance from the global aggregation models according to the characteristics of their local data. In terms of task personalization, MPCL provides different personalized global models to handle the corresponding RSTs. For participants with different RSTs, it can be implemented in the multi-task collaborative training strategy to explore the connection between different tasks. To demonstrate the feasibility of MPCL, we take remote sensing image classification as a case study and provide a detailed feasibility scheme. We constructed four benchmark datasets compliant with MPL and personalized MPL, including single-source and multi-source about SAR, hyperspectral and optical RSD. The experimental results demonstrate that our MPCL is superior in these four RSD, which ranked first in the competition with the classic or state-of-the-art MPL and personalized MPL algorithms. In addition, the scalability of MPCL is also verified on image segmentation RSTs of building and road extraction.

Federated learning has gained prominence as an effective solution for addressing data silos, enabling collaboration among multiple parties without sharing their data. However, existing federated learning algorithms often neglect the challenge posed by multi-modal data distribution. Moreover, previous pioneering work face limitations in encrypting the exponential and logarithmic operations of the objective function with multiple independent variables, and they rely on a third-party cooperator for encryption. To address these limitations, this paper introduces a universal multi-modal vertical federated learning framework. To tackle the data distribution challenge, we propose a two-step multi-modal transformer model that captures cross-domain semantic features effectively. For encryption, where traditional additively homomorphic encryption algorithms fall short by supporting only addition and multiplication, we employ bivariate Taylor series expansion to transform the objective function. Integrating these components, we present a comprehensive training and transmission protocol that eliminates the need for a third-party cooperator during the encryption process. Extensive experiments conducted on diverse video-text and image-text datasets validate the superior performance of our framework compared to state-of-the-art approaches, affirming its effectiveness in multi-modal vertical federated learning settings.

The pandemic of coronavirus disease 2019 (COVID-19) has led to a global public health crisis, which caused millions of deaths and billions of infections, greatly increasing the pressure on medical resources. With the continuous emergence of viral mutations, developing automated tools for COVID-19 diagnosis is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. However, medical images in a single site are usually of a limited amount or weakly labeled, while integrating data scattered around different institutions to build effective models is not allowed due to data policy restrictions. In this article, we propose a novel privacy-preserving cross-site framework for COVID-19 diagnosis with multimodal data, seeking to effectively leverage heterogeneous data from multiple parties while preserving patients' privacy. Specifically, a Siamese branched network is introduced as the backbone to capture inherent relationships across heterogeneous samples. The redesigned network is capable of handling semisupervised inputs in multimodalities and conducting task-specific training, in order to improve the model performance of various scenarios. The framework achieves significant improvement compared with state-of-the-art methods, as we demonstrate through extensive simulations on real-world datasets.

Multiparty learning (MPL) is an emerging framework for privacy-preserving collaborative learning. It enables individual devices to build a knowledge-shared model and remaining sensitive data locally. However, with the continuous increase of users, the heterogeneity gap between data and equipment becomes wider, which leads to the problem of model heterogeneous. In this article, we concentrate on two practical issues: data heterogeneous problem and model heterogeneous problem, and propose a novel personal MPL method named device-performance-driven heterogeneous MPL (HMPL). First, facing the data heterogeneous problem, we focus on the problem of various devices holding arbitrary data sizes. We introduce a heterogeneous feature-map integration method to adaptively unify the various feature maps. Meanwhile, to handle the model heterogeneous problem, as it is essential to customize models for adapting to the various computing performances, we propose a layer-wise model generation and aggregation strategy. The method can generate customized models based on the device's performance. In the aggregation process, the shared model parameters are updated through the rules that the network layers with the same semantics are aggregated with each other. Extensive experiments are conducted on four popular datasets, and the result demonstrates that our proposed framework outperforms the state of the art (SOTA).

Privacy-preserving collaborative learning enables resource-constrained edge devices (e.g., Internet of Things (IoT) devices and smartphones) to build a knowledge-shared model while keeping individual data locally, achieving privacy preservation by designing an effective communication protocol. However, the learning paradigm raises high requirements for aligned input features of models, which is hard to realize in complicated IoT scenarios with various monitoring indicators. In this article, we propose a novel collaborative learning framework that is tolerant of IoT devices with unaligned feature spaces. Local bilevel optimizations for both model parameters and input features are performed iteratively in the training phase, in which the internal correlations of local sensor data provide additional guidance for the feature inference and completion. The scheme breaks hardware boundaries among various IoT devices in collaboration with the assistance of model inversion inference, which gains a new perspective on the utilization of model confidentiality and requires minimal modifications to the existing collaborative learning process. The framework achieves significant improvement compared with state-of-the-art methods, as we demonstrate through extensive simulations on real-world data sets.

Multi-party learning provides an effective approach for training a machine learning model, e.g., deep neural networks (DNNs), over decentralized data by leveraging multiple decentralized computing devices, subjected to legal and practical constraints. Different parties, so-called local participants, usually provide heterogenous data in a decentralized mode, leading to non-IID data distributions across different local participants which pose a notorious challenge for multi-party learning. To address this challenge, we propose a novel heterogeneous differentiable sampling (HDS) framework. Inspired by the dropout strategy in DNNs, a data-driven network sampling strategy is devised in the HDS framework, with differentiable sampling rates which allow each local participant to extract from a common global model the optimal local model that best adapts to its own data properties so that the size of the local model can be significantly reduced to enable more efficient inference. Meanwhile, co-adaptation of the global model via learning such local models allows for achieving better learning performance under non-IID data distributions and speeds up the convergence of the global model. Experiments have demonstrated the superiority of the proposed method over several popular multi-party learning techniques in the multi-party settings with non-IID data distributions.

The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed parties such as different institutions and may not be directly gathered and integrated due to various data policy constraints. As a result, some parties may suffer from insufficient data available for training machine learning models. In this article, we propose a multiparty dual learning (MPDL) framework to alleviate the problem of limited data with poor quality in an isolated party. Since the knowledge-sharing processes for multiple parties always emerge in dual forms, we show that dual learning is naturally suitable to handle the challenge of missing data, and explicitly exploits the probabilistic correlation and structural relationship between dual tasks to regularize the training process. We introduce a feature-oriented differential privacy with mathematical proof, in order to avoid possible privacy leakage of raw features in the dual inference process. The approach requires minimal modifications to the existing multiparty learning structure, and each party can build flexible and powerful models separately, whose accuracy is no less than nondistributed self-learning approaches. The MPDL framework achieves significant improvement compared with state-of-the-art multiparty learning methods, as we demonstrated through simulations on real-world datasets.

Federated learning (FL) unites multiple participants to collaboratively learn a global consensus model on the centralized server by aggregating their individual models trained locally on clients. To meet the goal of obtaining an optimal model, sufficient labeled data and myriad communications are required during training. However, the major problems are the limited budget for manually annotating unlabeled instances and the restricted bandwidth of server and clients. This article presents a communication-efficient federated active semi-supervised learning (CEFedASSL) framework that unites active learning (AL) clients and a semi-supervised learning (SSL) client to train models on unlabeled data while achieving communication efficiency. In each AL client, different query strategies are, respectively, applied for the local model to obtain a more robust model and query only the optimal samples which significantly reduces the cost of annotation. Subsequently, these optimal samples are encrypted as input to fine-tune the pretrained model of the SSL client by performing self-training, thereby enhancing the model performance while preserving the privacy of data. Furthermore, we propose an efficient selective aggregation strategy to reduce the communication cost between clients and the server. Empirical experiments on four different learning tasks demonstrate that the proposed CEFedASSL distinctively outperforms the common FL algorithms in terms of both model performance and communication costs.

Detecting anomalies in videos is a fundamental issue in public security. The majority of existing deep learning methods often perform anomaly detection based on the behavior or the trajectory of a single target. However, due to the overlaps of the crowd and the low-resolution of monitoring images, the segmentation of population is hard to implement and the features cannot be learned thoroughly, which make the methods be easily disturbed by visual elements and thus may lead to false detection sometimes. To tackle these problems, we propose the influence-aware attention to learn the representative attributes of the whole crowd. Walking pedestrians can be divided into numbers of flows, and in this paper, we aim to measure the consistency of movement patterns in the same stream and the interactions between different streams. Meanwhile, great importance is given to the relation between pedestrians and the circumstance for certain anomalies occur as a result of environmental issues. Specifically, the influence-aware attention module is composed of the motion attention and the location attention, which is designed to quantify the relations in the scene from spatial and temporal aspects. For the lack of abnormal samples, we utilize a dual generator-based framework to learn interactions among normal scenes. Experimental results on six benchmarks verify the effectiveness and robustness of our proposed method.

Representing nodes in a network as low-dimensional dense vectors can facilitate the analysis of complex networks, which is a challenging task and has attracted increasing attention. However, in the real world, networks are changing over time, such as cooperation in citation networks and communication in email networks. Most of the recent embedding methods only focus on static networks. Thus they ignore the critical temporal information, which serves as a supplement to structure information and has been proved to improve the quality of node embedding. In this work, we propose an unsupervised deep learning model called DTINE, which explores temporal information for further enhancing the robustness of node representations in dynamic networks. To preserve network topology, we pertinently design a temporal weight and sampling strategy to extract features from the neighborhoods. An attention mechanism will be applied on the recurrent neural network to measure the contributions of historical information and capture the evolution of the networks. Experimental results on four real-world networks demonstrate that the proposed method achieves better performance than state-of-the-art methods.

With the prevalence of the Internet, online reviews have become a valuable information resource for people. However, the authenticity of online reviews remains a concern, and deceptive reviews have become one of the most urgent network security problems to be solved. Review spams will mislead users into making suboptimal choices and inflict their trust in online reviews. Most existing research manually extracted features and labeled training samples, which are usually complicated and time-consuming. This paper focuses primarily on a neglected emerging domain - movie review, and develops a novel unsupervised spam detection model with an attention mechanism. By extracting the statistical features of reviews, it is revealed that users will express their sentiments on different aspects of movies in reviews. An attention mechanism is introduced in the review embedding, and the conditional generative adversarial network is exploited to learn users’ review style for different genres of movies. The proposed model is evaluated on movie reviews crawled from Douban, a Chinese online community where people could express their feelings about movies. The experimental results demonstrate the superior performance of the proposed approach.