This paper introduces DGFedRS, a Federated Recommender System based on Diffusion Augmentation and Guided Denoising. The system aims to address the challenges of data sparsity and the lack of diversity in latent data distributions in sequential recommender systems. DGFedRS pre-trains a diffusion model using recommender data to generate interaction sequences, expanding sparse user-item interactions in the discrete space. A guided denoising strategy is employed to preserve user-specific preferences during the generation process. Additionally, a noise control strategy is designed to minimize the impact of noise on personalized information. A stepwise scheduling strategy is used to input generated data into the sequential recommender model based on its challenge levels. The effectiveness of DGFedRS is demonstrated through experiments on three real-world datasets, showing significant improvements over existing baselines. The contributions of the work include the design of a federated recommender system, the guided denoising strategy, and the noise control and stepwise scheduling strategies.This paper introduces DGFedRS, a Federated Recommender System based on Diffusion Augmentation and Guided Denoising. The system aims to address the challenges of data sparsity and the lack of diversity in latent data distributions in sequential recommender systems. DGFedRS pre-trains a diffusion model using recommender data to generate interaction sequences, expanding sparse user-item interactions in the discrete space. A guided denoising strategy is employed to preserve user-specific preferences during the generation process. Additionally, a noise control strategy is designed to minimize the impact of noise on personalized information. A stepwise scheduling strategy is used to input generated data into the sequential recommender model based on its challenge levels. The effectiveness of DGFedRS is demonstrated through experiments on three real-world datasets, showing significant improvements over existing baselines. The contributions of the work include the design of a federated recommender system, the guided denoising strategy, and the noise control and stepwise scheduling strategies.