The increasingly stringent data privacy regulations limit the development of person re-identification (ReID) because person ReID training requires centralizing an enormous amount of data that contains sensitive personal information. To address this problem, we introduce federated person re-identification (FedReID) -- implementing federated learning, an emerging distributed training method, to person ReID. FedReID preserves data privacy by aggregating model updates, instead of raw data, from clients to a central server. Furthermore, we optimize the performance of FedReID under statistical heterogeneity via benchmark analysis. We first construct a benchmark with an enhanced algorithm, two architectures, and nine person ReID datasets with large variances to simulate the real-world statistical heterogeneity. The benchmark results present insights and bottlenecks of FedReID under statistical heterogeneity, including challenges in convergence and poor performance on datasets with large volumes. Based on these insights, we propose three optimization approaches: (1) We adopt knowledge distillation to facilitate the convergence of FedReID by better transferring knowledge from clients to the server; (2) We introduce client clustering to improve the performance of large datasets by aggregating clients with similar data distributions; (3) We propose cosine distance weight to elevate performance by dynamically updating the weights for aggregation depending on how well models are trained in clients. Extensive experiments demonstrate that these approaches achieve satisfying convergence with much better performance on all datasets. We believe that FedReID will shed light on implementing and optimizing federated learning on more computer vision applications.
翻译:越来越严格的数据隐私条例限制了个人再识别(ReID)的发展,因为个人再识别培训要求集中大量包含敏感个人信息的数据。为了解决这一问题,我们向个人再识别(FedReID)引入了联邦再识别(FedReID) -- -- 实施联邦化学习,这是新出现的分散培训方法,通过从客户到中央服务器的原始数据,而不是原始数据,来保护数据隐私。此外,我们通过基准分析,在统计差异性差异下优化了FedReID的业绩。我们首先用强化算法、两个架构和九个人再识别数据集来构建一个基准,这些基准将具有巨大差异的重新识别(FedReID),以模拟真实世界统计性差异。基准结果显示FedReID在统计性差异性下有洞察力和瓶颈,包括大量数据集的趋同和不良表现。基于这些洞察力,我们建议三种优化方法:(1) 我们采用知识蒸馏法,通过将知识从客户向服务器更好地转移知识,促进FedReID的趋同;(2) 我们引入客户群集,以更精确的眼光改进大型的视野应用,以提升客户的距离衡量业绩的进度。我们提议,不断更新的数据模型将如何改进客户如何更新。