@inproceedings{
jia2024adaptive,
title={Adaptive Hierarchical Aggregation for Federated Object Detection},
author={Ruofan Jia and Weiying Xie and Jie Lei and Yunsong Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=opVCEvRsTM}
}
In practical object detection scenarios, distributed data and stringent privacy protections significantly limit the feasibility of traditional centralized training methods. Federated learning (FL) emerges as a promising solution to this dilemma. Nonetheless, the issue of data heterogeneity introduces distinct challenges to federated object detection, evident in diminished object perception, classification, and localization abilities. In response, we introduce a task-driven federated learning methodology, dubbed Adaptive Hierarchical Aggregation (FedAHA), tailored to overcome these obstacles. Our algorithm unfolds in two strategic phases from shallow-to-deep layers: (1) Structure-aware Aggregation (SAA) aligns feature extractors during the aggregation phase, thus bolstering the global model's object perception capabilities; (2) Convex Semantic Calibration (CSC) leverages convex function theory to average semantic features instead of model parameters, enhancing the global model's classification and localization precision.  We demonstrate experimentally and theoretically the effectiveness of the proposed two modules respectively. Our method consistently outperforming the state-of-the-art methods across multiple valuable application scenarios. Moreover, we build a real FL system using Raspberry Pis to demonstrate that our approach achieves a good trade-off between performance and efficiency.
Adaptive Hierarchical Aggregation for Federated Object Detection
Ruofan Jia Weiying Xie Jie Lei Yunsong Li