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

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.