  In recent years, the attention towards One-Shot Federated Learning (OSFL) has
been driven by its capacity to minimize communication. With the development of
the diffusion model (DM), several methods employ the DM for OSFL, utilizing
model parameters, image features, or textual prompts as mediums to transfer the
local client knowledge to the server. However, these mediums often require
public datasets or the uniform feature extractor, significantly limiting their
practicality. In this paper, we propose FedDEO, a Description-Enhanced One-Shot
Federated Learning Method with DMs, offering a novel exploration of utilizing
the DM in OSFL. The core idea of our method involves training local
descriptions on the clients, serving as the medium to transfer the knowledge of
the distributed clients to the server. Firstly, we train local descriptions on
the client data to capture the characteristics of client distributions, which
are then uploaded to the server. On the server, the descriptions are used as
conditions to guide the DM in generating synthetic datasets that comply with
the distributions of various clients, enabling the training of the aggregated
model. Theoretical analyses and sufficient quantitation and visualization
experiments on three large-scale real-world datasets demonstrate that through
the training of local descriptions, the server is capable of generating
synthetic datasets with high quality and diversity. Consequently, with
advantages in communication and privacy protection, the aggregated model
outperforms compared FL or diffusion-based OSFL methods and, on some clients,
outperforms the performance ceiling of centralized training.
