  Federated learning (FL) has been recognized as one of the most promising
solutions for distributed machine learning (ML). In most of the current
literature, FL has been studied for supervised ML tasks, in which edge devices
collect labeled data. Nevertheless, in many applications, it is impractical to
assume existence of labeled data across devices. To this end, we develop a
novel methodology, Cooperative Federated unsupervised Contrastive Learning
(CF-CL), for FL across edge devices with unlabeled datasets. CF-CL employs
local device cooperation where data are exchanged among devices through
device-to-device (D2D) communications to avoid local model bias resulting from
non-independent and identically distributed (non-i.i.d.) local datasets. CF-CL
introduces a push-pull smart data sharing mechanism tailored to unsupervised FL
settings, in which, each device pushes a subset of its local datapoints to its
neighbors as reserved data points, and pulls a set of datapoints from its
neighbors, sampled through a probabilistic importance sampling technique. We
demonstrate that CF-CL leads to (i) alignment of unsupervised learned latent
spaces across devices, (ii) faster global convergence, allowing for less
frequent global model aggregations; and (iii) is effective in extreme
non-i.i.d. data settings across the devices.
