Data imbalance across clients in federated learning often leads to different local feature space partitions, harming the global model's generalization ability. Existing methods either employ knowledge distillation to guide consistent local training or performs procedures to calibrate local models before aggregation. However, they overlook the ill-posed model aggregation caused by imbalanced representation learning. To address this issue, this paper presents a cross-silo feature space alignment method (FedFSA), which learns a unified feature space for clients to bridge inconsistency. Specifically, FedFSA consists of two modules, where the in-silo prototypical space learning (ISPSL) module uses predefined text embeddings to regularize representation learning, which can improve the distinguishability of representations on imbalanced data. Subsequently, it introduces a variance transfer approach to construct the prototypical space, which aids in calibrating minority classes feature distribution and provides necessary information for the cross-silo feature space alignment (CSFSA) module. Moreover, the CSFSA module utilizes augmented features learned from the ISPSL module to learn a generalized mapping and align these features from different sources into a common space, which mitigates the negative impact caused by imbalanced factors. Experimental results from three datasets verified that FedFSA improves the consistency between diverse spaces on imbalanced data, which results in superior performance compared to existing methods.

Causal inference has emerged as a promising approach to mitigate long-tail classification by handling the biases introduced by class imbalance. However, along with the change of advanced backbone models from Convolutional Neural Networks (CNNs) to Visual Transformers (ViT), existing causal models may not achieve an expected performance gain. This paper investigates the influence of existing causal models on CNNs and ViT variants, highlighting that ViT's global feature representation makes it hard for causal methods to model associations between fine-grained features and predictions, which leads to difficulties in classifying tail classes with similar visual appearance. To address these issues, this paper proposes TSCNet, a two-stage causal modeling method to discover fine-grained causal associations through multi-scale causal interventions. Specifically, in the hierarchical causal representation learning stage (HCRL), it decouples the background and objects, applying backdoor interventions at both the patch and feature level to prevent model from using class-irrelevant areas to infer labels which enhances fine-grained causal representation. In the counterfactual logits' bias calibration stage (CLBC), it refines the optimization of model's decision boundary by adaptive constructing counterfactual balanced data distribution to remove the spurious associations in the logits caused by data distribution. Extensive experiments conducted on various long-tail benchmarks demonstrate that the proposed TSCNet can eliminate multiple biases introduced by data imbalance, which outperforms existing methods.

The clustering performance of Fuzzy Adaptive Resonance Theory (Fuzzy ART) is highly dependent on the preset vigilance parameter, where deviations in its value can lead to significant fluctuations in clustering results, severely limiting its practicality for non-expert users. Existing approaches generally enhance vigilance parameter robustness through adaptive mechanisms such as particle swarm optimization and fuzzy logic rules. However, they often introduce additional hyperparameters or complex frameworks that contradict the original simplicity of the algorithm. To address this, we propose Iterative Refinement Fuzzy Adaptive Resonance Theory (IR-ART), which integrates three key phases into a unified iterative framework: (1) Cluster Stability Detection: A dynamic stability detection module that identifies unstable clusters by analyzing the change of sample size (number of samples in the cluster) in iteration. (2) Unstable Cluster Deletion: An evolutionary pruning module that eliminates low-quality clusters. (3) Vigilance Region Expansion: A vigilance region expansion mechanism that adaptively adjusts similarity thresholds. Independent of the specific execution of clustering, these three phases sequentially focus on analyzing the implicit knowledge within the iterative process, adjusting weights and vigilance parameters, thereby laying a foundation for the next iteration. Experimental evaluation demonstrates that IR-ART improves tolerance to suboptimal vigilance parameter values while preserving the parameter simplicity of Fuzzy ART. Case studies visually confirm the algorithm’s self-optimization capability through iterative refinement, making it particularly suitable for non-expert users in resource-constrained scenarios.

3D spatial data management is increasingly vital across various application scenarios, such as GIS, digital twins, human atlases, and tissue imaging. However, the inherent complexity of 3D spatial data, primarily represented by 3D geometries in real-world applications, hinders the efficient evaluation of spatial relationships through resource-intensive geometric computations. Geometric simplification algorithms have been developed to reduce the complexity of 3D representations, albeit at the cost of querying accuracy. Previous work has aimed to address precision loss by leveraging the spatial relationship between the simplified and original 3D object representations. However, this approach relied on specialized geometric simplification algorithms tailored to regions with specific criteria. In this paper, we introduce a novel approach to achieve highly efficient and accurate 3D spatial queries, incorporating geometric computation and simplification. We present a generalized progressive refinement methodology applicable to general geometric simplification algorithms, involving accurate querying of 3D geometry data using low-resolution representations and simplification extents quantified using Hausdorff distances at the facet level. Additionally, we propose techniques for calculating and storing Hausdorff distances efficiently. Extensive experimental evaluations validate the effectiveness of the proposed method which outperforms state-of-the-art systems by a factor of 4 while minimizing computational and storage overhead.

Federated learning aims to facilitate collaborative training among multiple clients with data heterogeneity in a privacy-preserving manner, which either generates the generalized model or develops personalized models. However, existing methods typically struggle to balance both directions, as optimizing one often leads to failure in another. To address the problem, this article presents a method named personalized federated learning via cross silo prototypical calibration (pFedCSPC) to enhance the consistency of knowledge of clients by calibrating features from heterogeneous spaces, which contributes to enhancing the collaboration effectiveness between clients. Specifically, pFedCSPC employs an adaptive aggregation method to offer personalized initial models to each client, enabling rapid adaptation to personalized tasks. Subsequently, pFedCSPC learns class representation patterns on clients by clustering, averages the representations within each cluster to form local prototypes, and aggregates them on the server to generate global prototypes. Meanwhile, pFedCSPC leverages global prototypes as knowledge to guide the learning of local representation, which is beneficial for mitigating the data imbalanced problem and preventing overfitting. Moreover, pFedCSPC has designed a cross-silo prototypical calibration (CSPC) module, which utilizes contrastive learning techniques to map heterogeneous features from different sources into a unified space. This can enhance the generalization ability of the global model. Experiments were conducted on four datasets in terms of performance comparison, ablation study, in-depth analysis, and case study, and the results verified that pFedCSPC achieves improvements in both global generalization and local personalization performance via calibrating cross-source features and strengthening collaboration effectiveness, respectively.

Benefiting from the high mobility and the line-of-sight communications, unmanned aerial vehicles (UAVs) and high-altitude platform (HAP) can be, respectively, designated as the edge and cloud servers to aggregate the local and edge models in hierarchical federated learning (HFL). To enable energy-efficient HFL, we manoeuvre the trajectories and control the transmit powers of UAVs over multi-cell wireless networks. Meanwhile, as the channels are reused in different cells, inter-cell interference is inevitable during the aggregation at UAVs, leading to performance degradation of HFL. To tackle these issues, an algorithm based on multi-agent twin delayed deep deterministic policy gradient (MATD3) is proposed to minimize the overall energy consumption of UAVs during the training process. The simulation results show that the proposed MATD3-based algorithm performs much better than the baseline schemes.