FedPhD通过分层剪枝与自适应聚合，高效训练分布式扩散模型。
FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models
- 分层联邦学习结合数据同质性感知的聚合策略，缓解数据异构问题。
- 通信开销降低88%，FID得分提升至少34%，仅用56%资源。
- 适合资源受限设备上高效训练高质量图像生成模型。
联邦学习（FL）是一种分布式学习范式，在客户端数据上训练模型。其特别适用于扩散模型（DMs）的分布式训练，因为这类模型需要多样化数据以生成高质量图像。然而，与训练Transformer和卷积神经网络类似，当前在联邦环境中仍面临通信开销高、数据异构性强等挑战。现有研究对此关注较少。为此，我们提出一种新方法FedPhD，旨在高效实现联邦环境下的扩散模型训练。该方法采用分层联邦学习框架，结合同质性感知的模型聚合与选择策略，有效应对数据异构问题，并显著降低通信成本。同时，客户端端的结构化剪枝提升了计算效率，减少了模型存储需求。在多个数据集上的实验表明，FedPhD在保持高模型性能的同时，通信开销最高可降低88%，相比基线方法，FID得分提升至少34%，且仅需56%的总计算与通信资源。
原文摘要 · Abstract (English)
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 Fréchet 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。