Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. However, existing GDA methods typically assume that both source and target graphs exhibit homophily, leading existing methods to perform poorly when heterophily is present. Furthermore, the lack of labels in the target graph makes it impossible to assess its homophily level beforehand. To address this challenge, we propose a novel homophily-agnostic approach that effectively transfers knowledge between graphs with varying degrees of homophily. Specifically, we adopt a divide-and-conquer strategy that first separately reconstructs highly homophilic and heterophilic variants of both the source and target graphs, and then performs knowledge alignment separately between corresponding graph variants. Extensive experiments conducted on five benchmark datasets demonstrate the superior performance of our approach, particularly highlighting its substantial advantages on heterophilic graphs.

Prototype-based personalized federated learning methods have emerged as a promising strategy due to their ability to represent client-specific class characteristics effectively through learned class prototypes. These prototypes capture salient features of client-local data, facilitating personalized model adaptation. However, existing prototype-based aggregation strategies predominantly rely on weighted averaging, implicitly assuming prototype consistency across clients. This assumption neglects the intrinsic heterogeneity and non-independent and identically distributed (non-IID) nature of client data, compelling diverse local prototypes to align toward a singular global prototype and consequently causing significant aggregation bias. Motivated by observations from intra-class feature saliency analysis, we identify that clients inherently emphasize distinct feature regions even for the same class. To leverage this intra-class diversity, we introduce FedIC, a novel prototype clustering and collaborative classifier optimization approach. Specifically, FedIC first clusters prototypes based on intra-class similarity to form intra-class prototype subspaces, ensuring that aggregation occurs exclusively within each cluster, thus eliminating the bias stemming from forced global unification. To further exploit the benefits of intra-cluster collaboration, we quantify the combined predictive gains of classifiers from clients within the same cluster as a function of classifier combination weights. This targeted aggregation and collaborative optimization strategy effectively circumvents the bias introduced by global alignment. Extensive experiments under various non-IID settings show that FedIC significantly outperforms existing Prototype-based and Clustered PFL Methods.

The limited availability of high-quality training data poses a persistent challenge for synthetic aperture radar (SAR) target classification. Existing data augmentation methods mainly adopt a simplistic application of GAN-based style transfer techniques to directly synthesize pseudo-SAR images from optical images. However, our in-depth analysis of this cross-modal conversion reveals that such straightforward strategies primarily focus on transferring high-level semantic information (e.g., target shapes), thus failing to adequately capture the essential low-level features unique to SAR imagery (e.g., scattering textures). To address this inherent trade-off between high-level semantic preservation and low-level feature authenticity, we propose a Hierarchical Feature-Constrained GAN (HiFC-GAN) tailored for optical-to-SAR style transfer. Specifically, HiFC-GAN enhances the representation of low-level SAR features by introducing local texture contrast constraints at shallow layers, while introducing explicit feature mapping constraints at deeper layers to maintain high-level semantic consistency throughout the reconstruction process. Experimental results demonstrate that HiFC-GAN significantly outperforms existing GAN-based techniques in image generation quality, particularly improving the low-level feature authenticity of pseudo-SAR images. Moreover, the generated pseudo-SAR images further improve the performance of downstream target classification tasks, yielding accuracy gains ranging from 3.56% to 5.90% on average with mainstream CNN-based models.

Negative transfer (NF) is a critical challenge in personalized federated learning (pFL). Existing methods primarily focus on adapting local data distribution on the client side, which can only resist NF, rather than avoid NF itself. To tackle NF at its root, we investigate its mechanism through the lens of the global model, and argue that it is caused by update conflicts among clients during server aggregation. In light of this, we propose a conflict-free client update aggregation strategy (ConFREE), which enables us to avoid NF in pFL. Specifically, ConFREE guides the global update direction by constructing a conflict-free guidance vector through projection and utilizes the optimal local improvements of the worst-performing clients near the guidance vector to regularize server aggregation. This prevents the conflicting components of updates from transferring, achieving balanced updates across different clients. Notably, ConFREE is model-agnostic and can be straightforwardly adopted as a complement to enhance various existing NF-resistance methods implemented on the client side. Extensive experiments demonstrate substantial improvements to existing pFL algorithms by leveraging ConFREE.

Server aggregation conflict is a key challenge in personalized federated learning (PFL). While existing PFL methods have achieved significant progress with shallow base models (e.g., four-layer CNNs), they often overlook the negative impacts of deeper base models on personalization mechanisms. In this paper, we identify the phenomenon of deep model degradation in PFL, where as base model depth increases, the model becomes more sensitive to local client data distributions, thereby exacerbating server aggregation conflicts and ultimately reducing overall model performance. Moreover, we show that these conflicts manifest in insufficient global average updates and mutual constraints between clients. Motivated by our analysis, we proposed a two-stage conflict-aware layer-wise mitigation algorithm (FedCALM), which first constructs a conflict-free global update to alleviate negative conflicts, and then maximizes the benefits of all clients through a conflict-aware strategy. Notably, our method naturally leads to a selective mechanism that balances the tradeoff between clients involved in aggregation and the tolerance for conflicts. Consequently, it can boost the positive contribution to the clients even with the greatest conflicts with the global update. Extensive experiments across multiple datasets and deeper base models demonstrate that FedCALM outperforms four state-of-the-art (SOTA) methods by up to 9.88% and seamlessly integrates into existing PFL methods with performance improvements of up to 9.01%.

Many existing personalized federated learning (PFL) methods utilize clustering-based aggregation to group clients with similar data characteristics, improving model performance by promoting collaboration among clients with shared features. While this method effectively mitigates some challenges posed by data heterogeneity, it predominantly relies on static data features, making it challenging to capture the dynamic changes in client models during iterative training. This limitation impedes accurate clustering based on evolving model updates. To address this issue, we propose a Gradient-Driven Adaptive Clustering method in PFL (GradPFL), which more effectively captures the personalized deviations in locally updated models. Our approach also introduces an adaptive historical gradient mechanism that refines the clustering process by incorporating both current and past update characteristics. This enables more accurate model aggregation that adapts to ongoing changes in client models during training. Experimental results demonstrate that GradPFL outperforms existing clustering-based PFL methods, especially in more complex non-IID environments.

Source-Free Domain Adaptation (SFDA) aims to train a target model without source data, and the key is to generate pseudo-labels using a pre-trained source model. However, we observe that the source model often produces highly uncertain pseudo-labels for hard samples, particularly those heavily affected by domain shifts, leading to these noisy pseudo-labels being introduced even before adaptation and further reinforced through parameter updates. Additionally, they continuously influence neighbor samples through propagation in the feature space. To eliminate the issue of noise accumulation, we propose a novel Progressive Curriculum Labeling (ElimPCL) method, which iteratively filters trustworthy pseudo-labeled samples based on prototype consistency to exclude high-noise samples from training. Furthermore, a Dual MixUP technique is designed in the feature space to enhance the separability of hard samples, thereby mitigating the interference of noisy samples on their neighbors. Extensive experiments validate the effectiveness of ElimPCL, achieving up to a 3.4% improvement on challenging tasks compared to state-of-the-art methods.

Existing SAR image classification methods based on Contrastive Learning often rely on sample generation strategies designed for optical images, failing to capture the distinct semantic and physical characteristics of SAR data. To address this, we propose Physics-Driven Contrastive Mutual Learning for SAR Classification (PCM-SAR), which incorporates domain-specific physical insights to improve sample generation and feature extraction. PCM-SAR utilizes the gray-level co-occurrence matrix (GLCM) to simulate realistic noise patterns and applies semantic detection for unsupervised local sampling, ensuring generated samples accurately reflect SAR imaging properties. Additionally, a multi-level feature fusion mechanism based on mutual learning enables collaborative refinement of feature representations. Notably, PCM-SAR significantly enhances smaller models by refining SAR feature representations, compensating for their limited capacity. Experimental results show that PCM-SAR consistently outperforms SOTA methods across diverse datasets and SAR classification tasks.

The rapid growth of publicly available data has fueled deep learning advancements but also raises concerns about unauthorized data usage. Unlearnable Examples (UEs) have emerged as a data protection strategy that introduces imperceptible perturbations to prevent unauthorized learning. However, most existing UE methods produce perturbations strongly tied to specific training sets, leading to a significant drop in unlearnability when applied to unseen data or tasks. In this paper, we argue that for broad applicability, UEs should maintain their effectiveness across diverse application scenarios. To this end, we conduct the first comprehensive study on the transferability of UEs across diverse and practical yet demanding settings. Specifically, we identify key scenarios that pose significant challenges for existing UE methods, including varying styles, out-of-distribution classes, resolutions, and architectures. Moreover, we propose $\textbf{Versatile Transferable Generator}$ (VTG), a transferable generator designed to safeguard data across various conditions. Specifically, VTG integrates Adversarial Domain Augmentation (ADA) into the generator’s training process to synthesize out-of-distribution samples, thereby improving its generalizability to unseen scenarios. Furthermore, we propose a Perturbation-Label Coupling (PLC) mechanism that leverages contrastive learning to directly align perturbations with class labels. This approach reduces the generator’s reliance on data semantics, allowing VTG to produce unlearnable perturbations in a distribution-agnostic manner. Extensive experiments demonstrate the effectiveness and broad applicability of our approach. Code is available at https://github.com/zhli-cs/VTG.

User requests in edge computing environments are inherently decentralized and dynamic, posing significant challenges for efficient and adaptive service replica placement. To address this, we formulate the service replica placement problem in an edge-cloud collaborative environment, explicitly incorporating the spatio-temporal distribution of user requests. By capturing spatial and temporal correlations, we predict future request patterns to enable forward-looking replica placement. Given the NP-hard nature of the optimization problem, we design a DRL algorithm that optimizes replica placement decisions based on predictive modeling. To validate our approach, we conduct extensive experiments on real-world datasets across two typical application scenarios―grid-based and graph-based request distributions. Experimental results show our method reduces average response latency by up to 59.6% and boosts service provider profitability by 4.85% compared to reactive and temporal-only baselines. The proposed framework provides a novel and effective solution for proactive service provisioning in edge computing environments.

Most existing synthetic aperture radar (SAR) ship classification technologies heavily rely on correctly labeled data, ignoring the discriminate features of unlabeled SAR ship images. Even though researchers try to enrich CNN-based features by introducing traditional handcrafted features, existing methods easily cause information redundancy and fail to capture the interaction between them. To address these issues, we propose a novel dual-stream contrastive predictive network (DCPNet), which consists of two asymmetric tasks and a false negative sample elimination module. The first task is to construct positive sample pairs, guiding the core encoder to learn more general representations. The second task is to encourage adaptive capture of the correspondence between deep features and handcrafted features, achieving knowledge transfer within the model, and effectively improving the redundancy caused by the feature fusion. To increase the separability between clusters, we also design a cluster-level task. The experimental results on OpenSARShip and FUSAR-Ship datasets demonstrate the improvement in classification accuracy of supervised models and confirm the capability of learning effective representations of DCPNet.

In current synthetic aperture radar (SAR) object classification, one of the major challenges is the severe overfitting issue due to the limited dataset (few-shot) and noisy data. Considering the advantages of knowledge distillation as a learned label smoothing regularization, this paper proposes a novel Double Reverse Regularization Network based on Self-Knowledge Distillation (DRRNet-SKD). Specifically, through exploring the effect of distillation weight on the process of distillation, we are inspired to adopt the double reverse thought to implement an effective regularization network by combining offline and online distillation in a complementary way. Then, the Adaptive Weight Assignment (AWA) module is designed to adaptively assign two reverse-changing weights based on the network performance, allowing the student network to better benefit from both teachers. The experimental results on OpenSARShip and FUSAR-Ship demonstrate that DRRNet-SKD exhibits remarkable performance improvement on classical CNNs, outperforming state-of-the-art self-knowledge distillation methods.

In recent years, since edge computing has improved the performance of transportation systems, research on edge-computing-enabled transportation systems has received widespread attention. However, most previous studies overlooked that task requests in transportation systems are unevenly distributed in time and space, which easily causes the overloading of edge servers, resulting in high response latency. To this end, we present a novel task offloading scheme based on graph neural network (GNN) and deep reinforcement learning (DRL) in edge-computing-enabled transportation systems (TransEdge). Specifically, we first propose an adaptive node placement algorithm to assign Internet of Things sensors to appropriate edge servers, thereby minimizing transmission latency. Then, an improved DRL scheme based on GNN is designed to capture the spatial features between sensors, aiming to improve the accuracy of task offloading decisions. Finally, we introduce a task forwarding strategy based on the greedy algorithm to achieve collaborative task offloading between different edge servers and overcome the system instability caused by a sudden surge in task requests. We conduct extensive experiments on two real-world traffic data sets. The results show that TransEdge reduces the response latency by at least 3.7% compared to four baselines while achieving a success rate of 99%.

Recently, the Industrial Internet of Things (IIoT) has shown great application value in environmental monitoring. However, it suffers from serious bottlenecks in energy and computing capability. To address them, researchers have made lots of effort. Nevertheless, they neglect either the edge–end collaboration or the impact of task queue backlog, resulting in low system revenue. To this end, we design a queue-aware computation offloading method based on DRL (QDRL). Specifically, we represent the long-term system operation as a multistage stochastic mixed-integer optimization problem (M-SMIP), which is further converted into a deterministic problem using Lyapunov optimization. Given that the resource allocation and computation offloading in this deterministic problem are strongly coupled and difficult to solve, we decompose this problem into two subproblems. Subsequently, a reinforcement learning scheme with actor–critic architecture is designed to solve these subproblems. The Actor module is designed based on a deep learning model and quantization strategy for generating computation offloading actions. The mathematical reasoning and learning-based methods are integrated as the Critic module for achieving resource allocation. Extensive simulation results show that the performance of QDRL surpasses four baselines and approaches the approximate optimal algorithm in terms of average task queue length, normalized real computation rate, and computation time.

Federated Learning (FL) constructs a distributed machine learning framework that involves multiple remote clients collaboratively training models. However in real-world situations, the emergence of non-Independent and Identically Distributed (non-IID) data makes the global model generated by traditional FL algorithms no longer meet the needs of all clients, and the accuracy is greatly reduced. In this paper, we propose a personalized federated multi-task learning method via similarity awareness (PFedSA), which captures the similarity between client data through model parameters uploaded by clients, thus facilitating collaborative training of similar clients and providing personalized models based on each client’s data distribution. Specifically, it generates the intrinsic cluster structure among clients and introduces personalized patch layers into the cluster to personalize the cluster model. PFedSA also maintains the generalization ability of models, which allows each client to benefit from nodes with similar data distributions when training data, and the greater the similarity, the more benefit. We evaluate the performance of the PFedSA method using MNIST, EMNIST and CIFAR10 datasets, and investigate the impact of different data setting schemes on the performance of PFedSA. The results show that in all data setting scenarios, the PFedSA method proposed in this paper can achieve the best personalization performance, having more clients with higher accuracy, and it is especially effective when the client’s data is non-IID.

Multi-feature SAR ship classification aims to build models that can process, correlate, and fuse information from both handcrafted and deep features. Although handcrafted features provide rich expert knowledge, current fusion methods inadequately explore the relatively significant role of handcrafted features in conjunction with deep features, the imbalances in feature contributions, and the cooperative ways in which features learn. In this paper, we propose a novel multi-feature collaborative fusion network with deep supervision (MFCFNet) to effectively fuse handcrafted features and deep features for SAR ship classification tasks. Specifically, our framework mainly includes two types of feature extraction branches, a knowledge supervision and collaboration module, and a feature fusion and contribution assignment module. The former module improves the quality of the feature maps learned by each branch through auxiliary feature supervision and introduces a synergy loss to facilitate the interaction of information between deep features and handcrafted features. The latter module utilizes an attention mechanism to adaptively balance the importance among various features and assign the corresponding feature contributions to the total loss function based on the generated feature weights. We conducted extensive experimental and ablation studies on two public datasets, OpenSARShip-1.0 and FUSAR-Ship, and the results show that MFCFNet is effective and outperforms single deep feature and multi-feature models based on previous internal FC layer and terminal FC layer fusion. Furthermore, our proposed MFCFNet exhibits better performance than the current state-of-the-art methods.

Current synthetic aperture radar (SAR) ship classification research mainly focuses on modifying deep convolutional neural networks (DCNNs) and injecting manual features on DCNNs. Yet, the weak robustness of individual models in high-risk scenarios makes it difficult to gain the trust of SAR experts. In this letter, an automated method of heterogeneous DCNNs model ensemble based on two-stage filtration (MetaBoost) is proposed, effectively achieving robustness and high accuracy recognition on SAR ship classification. The principle of MetaBoost is generating a pool of diverse heterogeneous classifiers, selecting a subset of the most diverse and accurate classifiers, and finally fusing meta-features from the optimal subset. MetaBoost is a self-configuring algorithm that automatically determines the optimal type and number of base classifiers to be combined. Extensive experiments on the OpenSARShip and FUSAR-Ship datasets show that MetaBoost significantly outperforms individual classifiers, traditional ensemble models, and feature injection techniques.

Traditional automatic speech recognition (ASR) systems usually get a sharp performance drop when noise presents in speech. To make a robust ASR, we introduce a new model using the multi-task learning deep neural networks (MTL-DNN) to solve the speech denoising task in feature level. In this model, the networks are initialized by pre-training restricted Boltzmann machines (RBM) and fine-tuned by jointly learning multiple interactive tasks using a shared representation. In multi-task learning, we choose a noisy-clean speech pair fitting task as the primary task and separately explore two constraints as the secondary tasks: phone label and phone cluster. In experiments, the denoised speech is reconstructed by the MTL-DNN using the noisy speech as input and it is respectively evaluated by the DNN-hidden Markov model (HMM) based and the Gaussian Mixture Model (GMM)-HMM based ASR systems. Results show that, using the denoised speech, the word error rate (WER) is respectively reduced by 53.14% and 34.84% compared with baselines. The MTL-DNN model also outperforms the general single-task learning deep neural networks (STL-DNN) model with a performance improvement of 4.93% and 3.88% respectively.