FedRecI2C: A Novel Federated Recommendation Framework Integrating Communication and Computation to Accelerate Convergence Under Communication Constraints

Qizhong Zheng and Xiujie Huang ()
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Qizhong Zheng: College of Cyber Security, Jinan University, Guangzhou 511436, China
Xiujie Huang: College of Information Science and Technology, Jinan University, Guangzhou 510632, China

Future Internet, 2025, vol. 17, issue 3, 1-23

Abstract: The federated recommender system (FRS) employs federated learning methodologies to create a recommendation model in a distributed environment, where clients share locally updated data with the server without exposing raw data and achieving privacy preservation. However, varying communication capabilities among devices restrict the participation of only a subset of clients in each round of federated training, resulting in slower convergence and requiring additional training rounds. In this work, we propose a novel federated recommendation framework, called FedRecI2C, which integrates communication and computation resources in the system. This framework accelerates convergence by utilizing not only communication-capable clients for federated training but also communication-constrained clients to leverage their computation and limited communication resources for further local training. This framework offers simplicity and flexibility, providing a plug-and-play architecture that effectively enhances the convergence speed in FRSs. It has demonstrated remarkable effectiveness in a wide range of FRSs when operating under diverse communication conditions. Extensive experiments are conducted to validate the effectiveness of FedRecI2C. Moreover, we provide in-depth analyses of the FedRecI2C framework, offering novel insights into the training patterns of FRSs.

Keywords: federated recommender system; convergence rate; communication resources; computation resources (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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