Federated Learning Without Labels

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 introduce a generalized federated self-supervised learning (FedSSL) framework and conduct in-depth empirical study of FedSSL based on the framework. Our study uncovers unique insights of FedSSL: 1) stop-gradient operation, previously reported to be essential, is not always necessary in FedSSL; 2) retaining local knowledge of clients in FedSSL is particularly beneficial for non-IID data. Inspired by the insights, we propose a new approach for model update, FedEMA.

We propose a novel federated unsupervised learning framework, FedU, to learn visual representation from decentralized data while preserving data prviacy. To tackle non-IID challenge, we propose two simple but effective methods: 1) We design the communication protocol to upload and update only the online encoders; 2) We introduce a new module to dynamically decide how to update predictors based on the divergence caused by non-IID.

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.