基于Benchmark分析的联邦人员再识别性能优化
Performance Optimization for Federated Person Re-identification via Benchmark Analysis
摘要：
联邦学习是一种保护隐私的机器学习技术，它可以在分散的客户端之间学习共享模型。它可以减轻个人重新识别的隐私问题，这是一项重要的计算机视觉任务。在这项工作中，我们实现了联邦学习的人重新识别（FedReID），并优化其性能受统计异质性在现实世界中的情况。我们首先构建了一个新的基准测试来研究FedReID的性能。该基准测试包括（1）9个数据集，这些数据集具有来自不同域的不同卷，以模拟现实中的异构情况，（2）两个联邦场景，以及（3）FedReID的增强联邦算法。基准测试分析表明，以数据集联合场景为代表的客户端-边缘-云架构比FedReID中的客户端-服务器架构具有更好的性能。它还揭示了FedReID在现实场景下的瓶颈，包括模型聚合中权重不平衡导致的大数据集性能不佳以及收敛方面的挑战。然后提出了两种优化方法：（1）针对权重不均衡的问题，提出了一种新的方法，根据每轮训练中客户端模型变化的规模动态改变权重;（2）为了便于收敛，采用知识蒸馏方法，利用公共数据集上客户端模型生成的知识对服务器端模型进行精炼。实验结果表明，我们的策略可以实现更好的收敛性能优越的所有数据集。我们相信，我们的工作将激励社区进一步探索联邦学习在现实世界场景中的更多计算机视觉任务上的实现。
英文摘要：
Federated learning is a privacy-preserving machine learning technique that learns a shared model across decentralized clients. It can alleviate privacy concerns of personal re-identification, an important computer vision task. In this work, we implement federated learning to person re-identification (FedReID) and optimize its performance affected by statistical heterogeneity in the real-world scenario. We first construct a new benchmark to investigate the performance of FedReID. This benchmark consists of (1) nine datasets with different volumes sourced from different domains to simulate the heterogeneous situation in reality, (2) two federated scenarios, and (3) an enhanced federated algorithm for FedReID. The benchmark analysis shows that the client-edge-cloud architecture, represented by the federated-by-dataset scenario, has better performance than client-server architecture in FedReID. It also reveals the bottlenecks of FedReID under the real-world scenario, including poor performance of large datasets caused by unbalanced weights in model aggregation and challenges in convergence. Then we propose two optimization methods: (1) To address the unbalanced weight problem, we propose a new method to dynamically change the weights according to the scale of model changes in clients in each training round; (2) To facilitate convergence, we adopt knowledge distillation to refine the server model with knowledge generated from client models on a public dataset. Experiment results demonstrate that our strategies can achieve much better convergence with superior performance on all datasets. We believe that our work will inspire the community to further explore the implementation of federated learning on more computer vision tasks in real-world scenarios.
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