  To eliminate the requirement of fully-labeled data for supervised model
training in traditional Federated Learning (FL), extensive attention has been
paid to the application of Self-supervised Learning (SSL) approaches on FL to
tackle the label scarcity problem. Previous works on Federated SSL generally
fall into two categories: parameter-based model aggregation (i.e., FedAvg,
applicable to homogeneous cases) or data-based feature sharing (i.e., knowledge
distillation, applicable to heterogeneous cases) to achieve knowledge transfer
among multiple unlabeled clients. Despite the progress, all of them inevitably
rely on some assumptions, such as homogeneous models or the existence of an
additional public dataset, which hinder the universality of the training
frameworks for more general scenarios. Therefore, in this paper, we propose a
novel and general method named Federated Self-supervised Learning with
Feature-correlation based Aggregation (FedFoA) to tackle the above limitations
in a communication-efficient and privacy-preserving manner. Our insight is to
utilize feature correlation to align the feature mappings and calibrate the
local model updates across clients during their local training process. More
specifically, we design a factorization-based method to extract the
cross-feature relation matrix from the local representations. Then, the
relation matrix can be regarded as a carrier of semantic information to perform
the aggregation phase. We prove that FedFoA is a model-agnostic training
framework and can be easily compatible with state-of-the-art unsupervised FL
methods. Extensive empirical experiments demonstrate that our proposed approach
outperforms the state-of-the-art methods by a significant margin.
