person re-identification

Person re-identification (ReID) is a critical computer vision problem which identifies individuals from non-overlapping cameras. Many recent works on person ReID achieve remarkable performance by extracting features from large amounts of data using deep neural networks. However, the growing awareness of privacy concerns limits the development of person ReID. Prior studies employ federated person ReID to learn from decentralized edges without sharing raw data, but they overlook the variation of identities in different camera views. Concerning this issue, we propose a federated unsupervised person ReID (FedUCA) that leverages camera information to improve learning from decentralized unlabeled data. Specifically, FedUCA jointly learns person ReID models by transmitting training updates instead of raw data. We generate pseudo-labels for unlabeled local datasets on edges by clustering them into multiple groups according to different cameras. We then introduce contrastive learning with an intra-camera loss and an inter-camera loss to enhance the discrimination ability. In extensive experiments on eight person ReID datasets, our proposed approach significantly outperforms the state-of-the-art federated learning based method. It improves performance by 6% to 32% on these datasets, and notably by over 25 % on large datasets. We hope this paper will shed light on optimizing federated learning across a broader range of multimedia applications.

We construct a new benchmark to investigate the performance of federated person re-identification (FedReID), which contains nine datasets with different volumes sourced from different domains to simulate the heterogeneous situation in reality. The benchmark analysis 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. To address these issues, we propose three optimization methods: 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.

We present FedUReID, a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy. FedUReID enables in-situ model training on edges with unlabeled data. A cloud server aggregates models from edges instead of centralizing raw data to preserve data privacy. Moreover, to tackle the problem that edges vary in data volumes and distributions, we personalize training in edges with joint optimization of cloud and edge. Specifically, we propose personalized epoch to reassign computation throughout training, personalized clustering to iteratively predict suitable labels for unlabeled data, and personalized update to adapt the server aggregated model to each edge.

We construct a new benchmark to investigate the performance of FedReID, which contains nine datasets with different volumes sourced from different domains to simulate the heterogeneous situation in reality. The benchmark analysis 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. To address these issues, 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.