Federated learning (FL) is a popular solution for distributed machine
learning (ML). While FL has traditionally been studied for supervised ML tasks,
in many applications, it is impractical to assume availability of labeled data
across devices. To this end, we develop Cooperative Federated unsupervised
Contrastive Learning ({\tt CF-CL)} to facilitate FL across edge devices with
unlabeled datasets. {\tt CF-CL} employs local device cooperation where either
explicit (i.e., raw data) or implicit (i.e., embeddings) information is
exchanged through device-to-device (D2D) communications to improve local
diversity. Specifically, we introduce a \textit{smart information push-pull}
methodology for data/embedding exchange tailored to FL settings with either
soft or strict data privacy restrictions. Information sharing is conducted
through a probabilistic importance sampling technique at receivers leveraging a
carefully crafted reserve dataset provided by transmitters. In the implicit
case, embedding exchange is further integrated into the local ML training at
the devices via a regularization term incorporated into the contrastive loss,
augmented with a dynamic contrastive margin to adjust the volume of latent
space explored. Numerical evaluations demonstrate that {\tt CF-CL} leads to
alignment of latent spaces learned across devices, results in faster and more
efficient global model training, and is effective in extreme non-i.i.d. data
distribution settings across devices.

通过采用合作式联邦非监督对比学习 (CF-CL)，在边缘设备之间进行交换未标记数据 / 嵌入的智能信息推送 - 拉取方法，以改善本地多样性，并在全球模型训练中实现更快速、更高效的结果，适用于数据分布不均的极端情况。