DualPFL: A Dual Sparse Pruning Method with Efficient Federated Learning for Edge-Based Object Detection  
[PERSON]  
1 School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,  
Shanghai 200240, China; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (Y.S.) 1  
[PERSON]  
1 School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,  
Shanghai 200240, China; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (Y.S.) 1  
[PERSON]  
1 School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,  
Shanghai 200240, China; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (S.D. ); [EMAIL_ADDRESS] (Y.S.) 1  
[PERSON]  
2 Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201203, China 2  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/10](https://www.mdpi.com/journals/pdf/10)]([https://www.mdpi.com/journals/pdf/10](https://www.mdpi.com/journals/pdf/10))  
in convolutional neural network (CNN) [10]. However, this method is primarily suited for the pruning needs of single-client models. In the scenario of FL, the heterogeneity of edge devices is another challenge [11; 12]. This may result in substantial differences between the local models trained on each device, thereby impacting the aggregation effect of the global model. Under such circumstances, a one-shot pruning strategy might be inadequate, as the importance of model parameters can vary significantly across different devices. Consequently, we urgently need a general and common framework that not only addresses the challenges posed by communication overhead but also quantitatively answers the following question: how can adaptive pruning strategies be designed to be suitable for diverse devices when engaging in federated training with heterogeneity? Several studies have attempted to tackle similar problems from various perspectives [13]. [PERSON] et al. [13] proposed a FedMask framework to achieve efficient inference. The key idea is to apply the learned binary mask to the fixed parameters of the local models. However, this approach determines the location of the binary mask based on the magnitude of the model parameters, regarding communication efficiency, without considering the impact of statistical heterogeneity. [PERSON] et al. [14] implemented personalized federation by customizing sparse models for each local device. They consider that statistical heterogeneity employs a fixed sparse pruning ratio without considering the impact of the changing training states of edge devices. To handle these limitations in terms of communication efficiency in heterogeneous FL scenarios, we propose a dataset-aware dual sparse federated learning and scale-based model aggregation method to achieve personalized models. On this basis, we introduce dynamic sparsity-based channel pruning that progressively increases the sparsity ratios on edge devices. This helps to reduce both computational and storage demands, effectively adapting to the resource constraints of edge devices. The contributions of this work are summarized as follows:  
1. We propose a novel communication-efficient federated learning framework for heterogeneous edge devices to perform classification and detection tasks. The framework considers the non-IID data stored by the device and achieves a dynamic balance between improved efficiency and good performance. 2. To enable personalization for local devices, we propose a data-aware sparse pre-training mechanism. Specifically, by calculating the inverse of the KL divergence of the local data distribution as the sparsity ratio, the personalized information of each device is incorporated into the construction of the heterogeneous binary mask. This allows the local model to better adapt to the private dataset. 3.