self-supervised learning

Develop federated learning systems and algorithms that do not require data labels on decentralized clients.

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.