FedDiffRec: A Module-wise Training Approach for Diffusion-Based Recommendation in Federated Learning

Abstract

Federated Learning (FL) has become a prominent framework for maintaining privacy in recommender systems by enabling decentralized model training. Despite its benefits, traditional Federated Recommender Systems (FRSs)—often relying on collaborative filtering or generative models such as Variational Autoencoders (VAEs)—face limitations in capturing complex user-item interactions, resulting in suboptimal performance. Recent advancements in diffusion-based models, exemplified by L-DiffRec, have demonstrated superior capability in modeling intricate patterns. However, these models encounter significant challenges in federated settings, including data heterogeneity and slow convergence. To address these limitations, this paper introduces FedDiffRec, a novel federated framework that employs a module-wise training strategy and utilize a pseudo-interaction pretraining. Specifically, the VAE module is first trained locally, followed by fine-tuning with a diffusion module. Additionally, a pseudo-interaction pretraining mechanism is proposed to address challenges related to model initialization and convergence. Experimental results show that FedDiffRec enhances the stability and performance of diffusion-based models in federated environments, effectively bridging the performance gap between advanced diffusion-based approaches and traditional FRSs.