FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
Federated Learning (FL), as a distributed learning paradigm, trains models
over distributed clients' data. FL is particularly beneficial for distributed
training of Diffusion Models (DMs), which are high-quality image generators
that require diverse data. However, challenges such as high communication costs
and data heterogeneity persist in training DMs similar to training Transformers
and Convolutional Neural Networks. Limited research has addressed these issues
in FL environments. To address this gap and challenges, we introduce a novel
approach, FedPhD, designed to efficiently train DMs in FL environments. FedPhD
leverages Hierarchical FL with homogeneity-aware model aggregation and
selection policy to tackle data heterogeneity while reducing communication
costs. The distributed structured pruning of FedPhD enhances computational
efficiency and reduces model storage requirements in clients. Our experiments
across multiple datasets demonstrate that FedPhD achieves high model
performance regarding Fr\'echet Inception Distance (FID) scores while reducing
communication costs by up to $88\%$. FedPhD outperforms baseline methods
achieving at least a $34\%$ improvement in FID, while utilizing only $56\%$ of
the total computation and communication resources.
