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Open Access

ARTICLE

Federated Dynamic Aggregation Selection Strategy-Based Multi-Receptive Field Fusion Classification Framework for Point Cloud Classification

Yuchao Hou1,2, Biaobiao Bai3, Shuai Zhao3, Yue Wang3, Jie Wang3, Zijian Li4,*
1 Shanxi Key Laboratory of Cryptography and Data Security, Shanxi Normal University, Taiyuan, 030031, China
2 State Key Laboratory of Public Big Data, Guizhou University, Guizhou, 550025, China
3 College of Computer Science and Artificial Intelligence, Shanxi Normal University, Taiyuan, 030031, China
4 College of Artificial Intelligence, Dalian Maritime University, Dalian, 116026, China
* Corresponding Author: Zijian Li. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.069789

Received 30 June 2025; Accepted 22 September 2025; Published online 07 November 2025

Abstract

Recently, large-scale deep learning models have been increasingly adopted for point cloud classification. However, these methods typically require collecting extensive datasets from multiple clients, which may lead to privacy leaks. Federated learning provides an effective solution to data leakage by eliminating the need for data transmission, relying instead on the exchange of model parameters. However, the uneven distribution of client data can still affect the model’s ability to generalize effectively. To address these challenges, we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework (FDASS-MRFCF). Specifically, we tackle these challenges with two key innovations: (1) During the client local training phase, we propose a Multi-Receptive Field Fusion Classification Model (MRFCM), which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion, enhancing the robustness of point cloud classification. (2) In the server aggregation phase, we introduce a Federated Dynamic Aggregation Selection Strategy (FDASS), which employs a hybrid strategy to average client model parameters, skip aggregation, or reallocate local models to different clients, thereby balancing global consistency and local diversity. We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks, demonstrating its effectiveness. The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.

Keywords

Point cloud classification; federated learning; multi-receptive field fusion; dynamic aggregation

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