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Federated learning (FL) has been recognized as one of the most promisingsolutions for distributed machine learning (ML). In most of the currentliterature, FL has been studied for supervised ML tasks, in which edge devicescollect labeled data. Nevertheless, in many applications, it is impractical toassume existence of labeled data across devices. To this end, we develop anovel methodology, Cooperative Federated unsupervised Contrastive Learning(CF-CL), for FL across edge devices with unlabeled datasets. CF-CL employslocal device cooperation where data are exchanged among devices throughdevice-to-device (D2D) communications to avoid local model bias resulting fromnon-independent and identically distributed (non-i.i.d.) local datasets. CF-CLintroduces a push-pull smart data sharing mechanism tailored to unsupervised FLsettings, in which, each device pushes a subset of its local datapoints to itsneighbors as reserved data points, and pulls a set of datapoints from itsneighbors, sampled through a probabilistic importance sampling technique. Wedemonstrate that CF-CL leads to (i) alignment of unsupervised learned latentspaces across devices, (ii) faster global convergence, allowing for lessfrequent global model aggregations; and (iii) is effective in extremenon-i.i.d. data settings across the devices.

Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange