FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification
The emergence of advanced surveillance systems has heightened the importance of person re-identification (re-ID), a critical component in intelligent surveillance and public safety. With the increasing demand for privacy-preserving technologies, federated learning (FL) has become a prominent approach that allows for collaborative model training without the need for centralized data collection. However, implementing FL in real-world re-ID applications presents significant challenges, primarily due to the statistical heterogeneity among clients stemming from non-IID (Independent and Identically Distributed) data distributions and the considerable communication overhead associated with frequent transmission of large-scale models.
To tackle these challenges effectively, researchers have introduced FedKLPR, a lightweight and communication-efficient federated learning framework tailored for person re-ID tasks. FedKLPR integrates three pivotal components designed to enhance model performance while reducing communication costs.
Key Components of FedKLPR
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KL-Divergence Regularization Loss (KLL):
This component plays a crucial role in constraining local updates by minimizing the discrepancy between local and global feature distributions. By alleviating the adverse effects of statistical heterogeneity, KLL significantly improves convergence stability, especially under non-IID settings. This is vital for ensuring that the model remains robust across diverse client data distributions.
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KL-Divergence-Prune Weighted Aggregation (KLPWA):
KLPWA enhances the aggregation process by integrating both the pruning ratio and distributional similarity. This innovation allows for the more effective aggregation of pruned local models, which is particularly beneficial in non-IID contexts. The result is a more resilient global model that can perform effectively despite the challenges posed by heterogeneous data.
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Cross-Round Recovery (CRR):
CRR employs a dynamic pruning control mechanism designed to prevent excessive model pruning, thus preserving model accuracy throughout iterative compression processes. This component is essential for maintaining the integrity and performance of the model as it undergoes continuous updates and adjustments.
Experimental Validation
The efficacy of FedKLPR has been demonstrated through rigorous experimental evaluations on eight benchmark datasets. Results indicate that FedKLPR achieves significant communication savings while simultaneously maintaining competitive accuracy levels. Specifically, when compared to state-of-the-art methods, FedKLPR has been shown to reduce communication costs by approximately 40% to 42% when utilizing the ResNet-50 architecture, all while achieving superior overall performance metrics.
In conclusion, FedKLPR represents a promising advancement in the realm of federated learning for person re-identification, addressing critical challenges related to data heterogeneity and communication efficiency. As intelligent surveillance systems continue to evolve, frameworks like FedKLPR will play an essential role in enhancing both privacy and performance in real-world applications.