Federated learning is a promising bridge that connects machine learning methods and multi-central medical data. It trains models using the local data, and protects the privacy of data. There are many methods for federated learning to aggregate models, especially personalized methods, which show relatively excellent performance. However, most of them excessively pay attention to global and local information while ignoring the random components during aggregating. That limits their performance in metrics like accuracy, specificity, and sensitivity. We propose a method (denoted by FedDiv) to make a balance between these metrics. The basic idea is to extract centralized features meanwhile filtering random components, and conduct personalized aggregation. These centralized features draw encoders’ attention, which enhances the performance of personalized models in specificity and sensitivity. Besides, they contain more global and local information, which is advantageous for personalized aggregation. Meanwhile, our personalized method preserves the local information as far as possible during aggregating models. These local information are the critical factor for the personalized models to perform better in accuracy. Finally, we validate this method in three public and one private medical datasets. Comparing with 14 federated methods, our method achieves the best performance in metrics including accuracy, specificity, sensitivity, and F1 score.

The real-time and accurate 3D segmentation of fruits in agricultural field is crucial for precision agriculture. In complex field scenarios, point clouds generated by depth cameras often have gaps and point dispersion problems, using the traditional farthest point sampling algorithm (FPS) makes it difficult to uniformly capture key features, and FPS requires extensive sorting operations, which reduces the speed of feature extraction. To optimize the sampling uniformity of point clouds with multiple holes in agricultural field scenes while enhancing feature extraction efficiency, this paper proposes a lightweight and efficient density-based grid parallel feature extraction network GPFEnet. First, an adaptive grid parallel grouping algorithm was designed to address the issue of low efficiency in capturing point cloud features caused by irregular point cloud data in complex field scenarios. Based on this fundamental algorithm, a density-based grid random sampling algorithm GDSample was proposed, which optimize sampling uniformity while improving sampling efficiency through support for parallel computation. Finally, the grid parallel feature extraction subnetwork GPFEnet was developed which can be embedded into point cloud deep learning networks. Testing results on field datasets show that the proposed GDSample algorithm is approximately 35 times faster than FPS, significantly accelerating feature extraction while ensuring more even coverage of key features. To confirm the superiority of GPFEnet on multi-hole fruit datasets, the GPFEnet was integrated into various cutting-edge point cloud deep learning networks, achieved a significant enhancement in speed of feature extraction while maintaining similar accuracy in fruit segmentation.

Offline safe reinforcement learning aims to learn policies that satisfy predefined safety constraints from static datasets. Existing sequence-model-based methods condition action generation on symmetric input tokens for return-to-go and cost-to-go, neglecting their intrinsic asymmetry: RTG serves as a flexible performance target, while CTG should represent a rigid safety boundary. This symmetric conditioning leads to unreliable constraint satisfaction, especially when encountering out-of-distribution cost trajectories. To address this, we propose Boundary-to-Region (B2R), a framework that enables asymmetric conditioning through cost signal realignment . B2R redefines CTG as a boundary constraint under a fixed safety budget, unifying the cost distribution of all feasible trajectories while preserving reward structures. Combined with rotary positional embeddings , it enhances exploration within the safe region. Experimental results show that B2R satisfies safety constraints in 35 out of 38 safety-critical tasks while achieving superior reward performance over baseline methods. This work highlights the limitations of symmetric token conditioning and establishes a new theoretical and practical approach for applying sequence models to safe RL.

Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical data among multiple institutes. Therefore, it has draw much attention due to its natural merit on privacy protection. However, when heterogenous medical data exists between different hospitals, federated learning usually has to face with degradation of performance. In the paper, we propose a new personalized framework of federated learning to handle the problem. It successfully yields personalized models based on awareness of similarity between local data, and achieves better tradeoff between generalization and personalization than existing methods. After that, we further design a differentially sparse regularizer to improve communication efficiency during procedure of model training. Additionally, we propose an effective method to reduce the computational cost, which improves computation efficiency significantly. Furthermore, we collect five real medical datasets, including two public medical image datasets and three private multi-center clinical diagnosis datasets, and evaluate its performance by conducting nodule classification, tumor segmentation, and clinical risk prediction tasks. Comparing with 14 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency.

Identifying the urban community structure is important for promoting smart city. In contrast to community detection in social network, the urban community construction involves both the information of population flow a nd t he distribution of public services. However, few methods take into account the two factors simultaneously in a reasonable manner. This causes several problems such as isolated nodes and geographical fragmentation in urban planning. To address the challenges of concerning the population flow a nd p ublic s ervices distribution in an unified framework, we formulate the identification problem of urban community structure as graph modularity optimization with constraints on the public services distribution. The combination of hierarchical clustering and node level adjustment strategies is applied, to capture the required information of community structure. In our framework, The attributed graph regards the geographical zones as nodes, the interaction between geographical zones as edges, the distribution of public services as attributes. Moreover, We evaluate our approach by using both the synthetic data set and the real-world data set. Results show the efficacy of the proposed method on identifying the urban community structure.