Federated learning is a distributed collaborative training framework that enables multiple clients to share model updates and jointly train deep neural networks without exchanging raw data. Although extensive research has explored data augmentation techniques in federated settings, the naturally non-IID data distributions among clients render blind augmentation prone to severe degradation of the learned model. To solve this problem, we suggest Responsive Augmentation and Approximate Update Method for Fed erated Learning with Non-IID Data (RAFed), aimed at alleviating feature shift in client samples. We leverage a Responsive Augmentation Method to accumulate shared data augmentation policy knowledge through local learning, guiding the policy gradient to consider the impact of data augmentation on unseen local data, and employ an Approximate Update Mechanism to reduce communication costs and achieve efficient policy search. To improve the adaptability of data augmentation policies to local data distributions, we introduce a Dynamic Adaptive Method for searching personalized augmentation policies tailored to heterogeneous clients. Experiments on four popular datasets show that RAFed achieves superior test accuracy and lower communication costs compared to related baselines while providing privacy advantages. The code is available via https://github.com/anonymously123-stcak/RAFed.

The field of recommender systems aims to predict users’ latent interests by analyzing their preferences and behaviors. However, privacy concerns about user data collection lead to challenges such as incomplete initial information and data sparsity. Federated learning has emerged to address these privacy issues in recommender systems. However, federated recommender systems face heterogeneity among edge devices regarding data features and sample sizes. Moreover, differences in computational and storage capabilities introduce communication overhead and processing delays during parameter aggregation at the third-party server. This article introduces a framework named FedRL , a reinforcement learning federated recommender system for efficient communication using Reinforcement Selector and Hypernet Generator, to address the proposed issues. The Reinforcement Selector dynamically selects participating edge devices and helps to maximize their use of local data resources. Meanwhile, Hypernet Generator optimizes communication bandwidth consumption during the federated learning parameter transmission, enabling rapid deployment and updates of new model architectures or hyperparameters. Furthermore, the framework incorporates item attributes as content embeddings in edge devices’ recommender models, enriching them with global information. Real-world dataset experiments demonstrate that the proposed solution balances recommender quality and communication efficiency. The code for this work is publicly available on GitHub: https://github.com/diyicheng/FedRL .

Sequential recommender systems often struggle with accurate personalized recommendations due to data sparsity issues. Existing works use variational autoencoders and generative adversarial network methods to enrich sparse data. However, they often overlook diversity in the latent data distribution, hindering the model’s generative capacity. This characteristic of generative methods can introduce additional noise in many cases. Moreover, retaining personalized user preferences through the generation process remains a challenge. This work introduces DGFedRS, a Federated Recommender System Based on Diffusion Augmentation and Guided Denoising, designed to capture the diversity in the latent data distribution while preserving user-specific information and suppressing noise. In particular, we pre-train the diffusion model using the recommender dataset and use a diffusion augmentation strategy to generate interaction sequences, expanding the sparse user-item interactions in the discrete space. To preserve user-specific preferences in the generated interactions, we employ a guided denoising strategy to guide the generation process during reverse diffusion. Subsequently, we design a noise control strategy to reduce the damage to personalized information during the diffusion process. Additionally, a stepwise scheduling strategy is devised to input generated data into the sequential recommender model based on their challenge levels. The success of the DGFedRS approach is demonstrated by thorough experiments conduct on three real-world datasets.

With the development of 5G communication and smart devices, the prosperity of online content has boosted Recommender System (RS) research. Due to the data scarcity problem, researchers employ knowledge transfer techniques to improve the accuracy of RS. Data sharing or data augmentation are promising methods for such a problem, but the data suffers from privacy leakage during sharing. Thus, federated learning has been adopted to collaboratively train recommender models while preserving data privacy. Federated Recommender System (FRS) combines federated learning and RS to provide distributed recommendation services to the users. However, the existing FRS suffers from massive communication and system heterogeneity, where the necessity of model transmission and the diversity of the clients bring significant communication overhead to the system. In this article, we propose Efficient Federated Recommender System with Adaptive Model Pruning and Momentum-based Batch Adjustment ( \(eFRSA^{2}\) ) to reduce the communication overhead of FRS. \(eFRSA^{2}\) contains two modules. Adaptive Model Pruning utilizes magnitude pruning to reduce the communication volume and adaptively modifies the compression ratios of different clients to maintain the model accuracy. Momentum-based Batch Adjustment adjusts the local training batch number by a similar method of gradient descent with momentum to align the local computation time of the clients and reduce the communication overhead. The experimental results demonstrate that \(eFRSA^{2}\) can reduce up to 90% communication volume and mitigate the system heterogeneity by over 75%, demonstrating the priority of \(eFRSA^{2}\) in training efficiency. Source code can be found at https://github.com/shhjwu5/eFRSA2 .