  Recently, semi-supervised federated learning (semi-FL) has been proposed to
handle the commonly seen real-world scenarios with labeled data on the server
and unlabeled data on the clients. However, existing methods face several
challenges such as communication costs, data heterogeneity, and training
pressure on client devices. To address these challenges, we introduce the
powerful diffusion models (DM) into semi-FL and propose FedDISC, a Federated
Diffusion-Inspired Semi-supervised Co-training method. Specifically, we first
extract prototypes of the labeled server data and use these prototypes to
predict pseudo-labels of the client data. For each category, we compute the
cluster centroids and domain-specific representations to signify the semantic
and stylistic information of their distributions. After adding noise, these
representations are sent back to the server, which uses the pre-trained DM to
generate synthetic datasets complying with the client distributions and train a
global model on it. With the assistance of vast knowledge within DM, the
synthetic datasets have comparable quality and diversity to the client images,
subsequently enabling the training of global models that achieve performance
equivalent to or even surpassing the ceiling of supervised centralized
training. FedDISC works within one communication round, does not require any
local training, and involves very minimal information uploading, greatly
enhancing its practicality. Extensive experiments on three large-scale datasets
demonstrate that FedDISC effectively addresses the semi-FL problem on non-IID
clients and outperforms the compared SOTA methods. Sufficient visualization
experiments also illustrate that the synthetic dataset generated by FedDISC
exhibits comparable diversity and quality to the original client dataset, with
a neglectable possibility of leaking privacy-sensitive information of the
clients.
