Federated recommendation systems aim to provide high-quality recommendations while protecting user privacy. However, existing federated recommendation algorithms typically use a unified item embedding framework, where all clients share the same item representations. Such approaches often fail to capture users’ personalized perceptions of the same item and struggle to reflect subtle changes in user preferences, limiting the effectiveness of personalized recommendations. Moreover, statistical heterogeneity among clients poses additional challenges for model optimization. To address these issues, we propose APFMRec, a personalized federated recommendation framework that enhances both personalization and global optimization. APFMRec incorporates an adaptive item embedding module to dynamically adjust item representations based on individual user preferences. In addition, a meta-learning update module is designed to mitigate statistical heterogeneity and improve collaborative optimization across clients. Extensive experiments on five real-world datasets demonstrate the effectiveness of APFMRec. The proposed framework consistently outperforms existing federated recommendation methods, achieving up to 8.1% improvement in HR@10 and 7.8% improvement in NDCG@10.

Federated few-shot learning (FedFSL) aims to enable the clients to obtain personalized generalization models for unseen categories with only a small number of referenceable samples in the distributed collaborative training paradigm. Most existing FedFSL-related algorithms suffer from domain bias and feature coupling in the presence of data heterogeneity and sample scarcity. In this work, we propose a collaborative feature representation disentanglement (CFRD) scheme for FedFSL to address these issues. After each client receives the global aggregation parameters, the original feature representation is decoupled into global communal features and local personality features with personalized bias representation, to maintain both global consistency and local relevance in the first feature representation disentanglement. On the few-shot metric space about the second feature representation disentanglement, category-independent information is encoded by class-specific and class-irrelevant reconstructions to separate the discriminative features. The proposed scheme collaboratively accomplishes global domain bias feature disentanglement and local category degradation feature disentanglement from client-wise and class-wise. Experiments on three few-shot benchmark datasets conforming to the FedFSL paradigm demonstrate that our proposed method outperforms state-of-the-art approaches in both global generality and local specificity.

Federated cross-domain recommendation aims to alleviate the problem of data sparsity and enable collaborative modeling of user behavior data from different platforms or institutions while ensuring data privacy. Most existing federated cross-domain recommendation methods rely on item IDs for modeling, ignoring the mining and utilization of item semantic information. In addition, due to the heterogeneity of data between different domains, the model is prone to domain bias and feature coupling problems during the aggregation process, which negatively impacts the recommendation performance. This paper proposes a dual-target federated cross-domain recommendation framework with semantic enhancement and disentangled contrastive learning. First, to utilize semantic information of items, item IDs features and text semantic features are jointly fused to enhance the item embedding representations. Second, we propose a user representation decoupling mechanism to explicitly decouple users preferences into shared and domain-specific preferences, thereby alleviating domain bias and feature coupling problems. Furthermore, we design a cross-domain contrastive learning module on the server side to enhance the consistency and transferability of shared representations between user representations across different domains. Experimental results show that the proposed algorithm performs significantly better than existing optimal methods on multiple real-world datasets, demonstrating its excellent performance in federated cross-domain recommendations.

Recommender systems play crucial roles in addressing the issue of information overload, but traditional centralized storage in recommendation poses significant privacy concerns. In recent years, federated learning has been successfully introduced into a recommendation, while these algorithms still encounter several challenges. First, real-world recommendation scenarios often suffer from sparse data, making it difficult for models to learn reliable representations. Second, data heterogeneity necessitates the design of personalized models to enhance recommendation performance. To address these challenges, we propose a federated recommendation approach based on graph neural networks, named federated personalized contrastive learning for recommendation. On the client side, we propose a contrastive learning approach to enhance the embedding quality of nodes (users or items) by maximizing positive similarities. Specifically, we formulate the concept of structural neighbors based on the graph structure and devise a contrastive learning objective. We treat nodes and their structural neighbors as positive pairs to better learn node representations. On the server side, we group users based on the learned representations and compute cluster-level federated models and a global model. Each user learns a personalized model by combining these two models. Extensive experiments on five real-world datasets demonstrate that the proposed algorithm outperforms existing methods in terms of performance.

Remote sensing (RS) scenarios usually involve sensitive geographic information on national security and regional development. In the commonly used centralized machine-learning paradigm, data dispersed in various locations are concentrated and processed on a single server, which is prone to privacy leakage and data security concerns. Besides, it is difficult to solve the high heterogeneity of RS images by simply applying federated learning (FL) algorithms to scene classification. In this article, we formulate a federated remote sensing scene classification (FedSC) framework, and design a customized neural architecture search (CNAS) to achieve both global generality for multiparty collaborative distributed training and local specificity for personalized RS scene customization. The proposed FedSC is generalizable to be implemented in any manually designed networks, network pruning strategies, or NAS methods related to remote sensing scene classification (RSSC). While the designed CNAS not only achieves collaborative distributed training in protecting participant data privacy to obtain a generalized global model, but also provides a customized local model for each participant that is more in line with the characteristics of private RS scenarios. Overall, the proposed FedSC$_{\textrm {CNAS}}$provides a novel federated collaborative training paradigm for RSSC in terms of data privacy, data heterogeneity, and personalized customization. Extensive analytical and comparative experiments on three benchmark RSSC datasets validate the versatility and effectiveness of our methods, and the proposed FedSC$_{\textrm {CNAS}}$exhibits superior competitiveness compared to state-of-the-art methods.

With the acquisition of large-scale remote sensing data and the development of deep learning, convolutional neural networks have achieved great progress in scene clas-sification tasks. However, the current networks greatly rely on the experience design of experts, and a single network cannot cope with multiple complex scene categories. In this paper, we design a novel evolutionary multitasking collaborative neural architecture search (EMCNAS) for remote sensing scene classification. EMCNAS mainly explores the similar features of different remote sensing scene classification tasks, and utilizes the uniformly encoded population to achieve implicit collaborative transfer. EM CNAS is able to adaptively determine the degree of exchange of genetic material based on the similarity of tasks in different scenarios, enabling more effective positive transfer. Compared with excellent manually designed neural networks and NAS peers, the proposed EMCNAS achieved competitive results on two remote sensing scene classification benchmark datasets UC Merced LandUse and NWPU-RESISC45 datasets. Ablation experiments also demonstrate the effectiveness of the adaptive collaborative transfer designed in EMCNAS.

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.

Few-shot aerial imagery segmentation refers to the task of segmenting specific objects in scenes that have not been encountered during training with a small amount of annotated data for reference. However, most existing few-shot segmentation algorithms are primarily designed for natural images, and there is still a lack of exploration in the context of remote sensing aerial imagery. In this article, we propose a novel multiscale self-attention aggregation network (MS2A2Net), dubbed MS2A2Net, to address the challenge of few-shot aerial image segmentation in terms of scarce data and network architecture. Specifically, we first incorporate the designed asymmetric momentum contrastive learning (AMCL) into the pre-training stage, to improve the representation capability of the backbone without the expensive labeled data. Then the frozen encoder is transferred to the downstream few-shot segmentation task as the feature embedding. In terms of network architecture, we design self-attention aggregation in multiscale feature fusion, to construct the dual correlation of foreground and background between support and query features at the pixel level. Besides, the coordinate attention is designed to rearrange the distribution of feature importance in both horizontal and vertical spatial order perspectives, which facilitates adaptive fusion with the multiscale features. To verify the availability of the proposed MS2A2Net, we also reconstructed two novel datasets dedicated to few-shot aerial image segmentation, called DLRSD-$4^{i}$and iSAID-$4^{i}$. The experimental results show that our approach MS2A2Net is superior in three few-shot benchmark aerial imagery segmentation datasets, which achieves competitive segmentation performance. Extensive ablation experiments also reflect the effectiveness and scalability of the proposed components and overall network architecture.

The existing few-shot scene classification (FSSC) algorithms have achieved satisfactory results, but they are limited by the paradigm of centralized machine learning, i.e., private remote sensing data need to be centralized on a certain server for training. However, remote sensing images generally contain sensitive information such as national security and company privacy, so it is realistically difficult to collect remote sensing data from all the parties. Therefore, there is a pressing requirement in FSSC for a novel paradigm to achieve multi-party collaborative learning without compromising remote sensing data privacy. In this paper, we formulate a novel personalized multi-party few-shot learning (PMPFSL) paradigm for remote sensing scene classification. In PMPFSL, different participants can achieve multi-party collaborative learning without sacrificing the privacy of their local data, and their respective local models are able to recognize the unseen remote sensing scene categories with a small number of labeled samples. Importantly, the proposed PMPFSL is applicable to various multi-party learning algorithms and few-shot scene classification networks. Moreover, to address the problems of local model overfitting and poor discriminability of few-shot metrics, we propose the personalized adaptive distillation (PAD) scheme and multi-scale feature matching network (MSFMNet) on PMPFSL, respectively. Specifically, each participant obtains the MSFMNet with initialization parameters, and implements a certain number of local training on their respective private machines. Global aggregation is subsequently achieved by uploading only the local models to the central server. In a new round of local training, the participants realize personalized data adaptation to the global model based on the PAD. Overall, the proposed PMPFSL customizes a personalized few-shot model for each participant that is more tailored to their respective remote sensing scenarios. The experimental results demonstrate that our PMPFSL is superior in three benchmark FSSC datasets. We also extensively studied and analyzed the contributions of PAD and MSFMNet in the proposed PMPFSL framework.

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.

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.

Endmember selection of hyperspectral images is a practical yet difficult task due to the high spectral resolution and low spatial resolution of the hyperspectral cameras. The paradigm of multitask optimization has been investigated over two decades, which aim to handle multiple tasks simultaneously. To address these issues, we propose a novel multitasking framework based on multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D). Specifically, we use a single population to simultaneously perform multiple subset selection tasks and apply it to a specific scene-the endmember selection of hyperspectral images. It is natural to consider that pixels in a homogeneous region of hyperspectral image as a task. Then, a within-task and between-task genetic transfer operator is constructed to reinforce the exchange of genetic material belonging to the same or different tasks for better and quicker search of the decision space. After that, this algorithm obtains a set of nondominated solutions for better decision of the active endmembers. Experiments on hyperspectral datasets show the effectiveness of our method in finding the real active endmembers.

Graph classification is a practical problem in many different domains including bioinformatics, chemoinformatics, social network analysis, and etc. For the graph classification task, the existing graph neural network approaches usually generate graph features using graph pooling at each step. However, this strategy of pooling only at the current step ignores the impact of self-loop. To eliminate this limitation, we propose a novel self-loop graph pooling strategy that can utilize the node information of the current step and the graph representation information of the previous step to generate an effective representation for the graph classification task. Further to measure the importance of self-loop, we also develop a gated approach, gated graph pooling with self-loop, that utilizes the simple fusion gate to enhance the representation capacity of the model. We evaluate our model on common benchmark datasets and experimental results have demonstrated the superior performance improvement on predictive accuracy.

The goal of network representation learning is to embed nodes so as to encode the proximity structures of a graph into a continuous low-dimensional feature space. In this paper, we propose a novel algorithm called node2hash based on feature hashing for generating node embeddings. This approach follows the encoder-decoder framework. There are two main mapping functions in this framework. The first is an encoder to map each node into high-dimensional vectors. The second is a decoder to hash these vectors into a lower dimensional feature space. More specifically, we firstly derive a proximity measurement called expected distance as target which combines position distribution and co-occurrence statistics of nodes over random walks so as to build a proximity matrix, then introduce a set of T different hash functions into feature hashing to generate uniformly distributed vector representations of nodes from the proximity matrix. Compared with the existing state-of-the-art network representation learning approaches, node2hash shows a competitive performance on multi-class node classification and link prediction tasks on three real-world networks from various domains.

Image registration is an important preprocessing step for many remote sensing image processing applications, and its result will affect the performance of the follow-up procedures. Establishing reliable matches is a key issue in point matching-based image registration. Due to the significant intensity mapping difference between remote sensing images, it may be difficult to find enough correct matches from the tentative matches. In this letter, particle swarm optimization (PSO) sample consensus algorithm is proposed for remote sensing image registration. Different from random sample consensus (RANSAC) algorithm, the proposed method directly samples the modal transformation parameter rather than randomly selecting tentative matches. Thus, the proposed method is less sensitive to the correct rate than RANSAC, and it has the ability to handle lower correct rate and more matches. Meanwhile, PSO is utilized to optimize parameter as its efficiency. The proposed method is tested on several multisensor remote sensing image pairs. The experimental results indicate that the proposed method yields a better registration performance in terms of both the number of correct matches and aligning accuracy.

The detection of shared community structure in multilayer network is an interesting and important issue that has attracted many researches. Traditional methods for community detection of single layer networks are not suitable for that of multilayer networks. In a previous work, the authors modeled the community discovery problem in multilayer network as a multiobjective one and devised a genetic algorithm to carry out it. In this paper, based on their model, we propose an improved multiobjective evolutionary approach MOEA-MultiNet for community detection in multilayer networks. The proposed MOEA-MultiNet is based on the framework of NSGA-II which employs the string-based representation scheme and synthesizes the genetic operation and local search to perform individual refinement. Experimental results on two real-world networks both demonstrate the ability and efficiency of the proposed MOEA-MultiNet in detecting community structure in multilayer networks.

The aim of influence maximization problem is to mine a small set of influential individuals in a complex network which could reach the maximum influence spread. In this paper, an efficient fitness function based on local influence is designed to estimate the influence spread. Then, we propose a discrete particle swarm optimization based algorithm to find the final set with the maximum value of the fitness function. In the proposed algorithm, discrete position and velocity are redefined and problem-specific update rules are designed. In order to accelerate the convergence, we introduce a degree-based population initialization method and a mutation learning based local search strategy. Experimental results compared with four comparison algorithms show that our proposed algorithm is able to efficiently find good-quality solutions.

The Near Space Communication System is a promising and burgeoning alternative solution to the modern world's increasing communication demand. The deployment of the airships is important to the performance of the Near Space Communication System, of which the coverage and speed are conflictive. In a real world application, this system is also usually faced with regular changes of the user distribution. In this paper, we present a Tide User Model which simulates those changes of the user distribution. To optimize the deployment of the airships on the Tide User Model, a memetic algorithm based on MOEA/D with particularly designed operators is proposed aiming at providing a set of solutions as good as possible for the decision maker. We carry out the experiments on which the proposed algorithm and MOEA/D with two regular operators are compared for different settings. The results show that the proposed memetic algorithm based on MOEA/D achieves satisfying results and has better performance on the Tide User Model.

Social balance property is an eminent feature of social networks. Many creative efforts concerning social balance have been done. This paper presents an optimization idea to solve the social balance of complex social networks. A single objective optimization model integrating the network balance and the community properties is proposed towards the social balance problem. A discrete particle swarm optimization algorithm is introduced to solve the proposed optimization model. Extensive experiments on synthetic and real-world signed networks demonstrate that the proposed model does make sense and the introduced optimization algorithm is promissing for solving the social balance problem.

Deep community can be detected by removing noise nodes or edges from a network. A centrality measure, named local Fiedler vector centrality is proposed for deep community detection. Algorithms to optimize local Fiedler vector centrality are either with high computation complexity or difficult to find the optimal solution of local Fiedler vector centrality. In this paper, a novel memetic algorithm is proposed to maximize local Fiedler vector centrality for deep community detection. Experiments of our proposed memetic algorithm on four real world networks demonstrate that our algorithm can find optimal solution of local Fiedler vector centrality and is effective to discover deep communities.

With the rapid expansion of the information on the Internet, recommender systems play an important role in filtering insignificant information and recommend satisfactory items to users. Accurately predicting the preference of users is the first priority of recommendation. Diversity is also an important objective in recommendation, which is achieved by recommending items from the so-called long tail of goods. Traditional recommendation techniques lay more emphasis on accuracy and overlook diversity. Simultaneously optimizing the accuracy and diversity is a multiobjective optimization problem, in which the two objectives are contradictory. In this paper, a multiobjective evolutionary algorithm based on decomposition is proposed for recommendation, which maximizes the predicted score and the popularity of items simultaneously. This algorithm returns lots of non-dominated solutions and each solution is a trade-off between the accuracy and diversity. The experiment shows that our algorithm can provide a series of recommendation results with different precision and diversity to a user.