Communication-efficient federated recommendation model based on many-objective evolutionary algorithm

•A novel communication-efficient federated recommendation model is proposed.•A many-objective evolutionary method is used to achieve parameter reduction.•Recommended performances and communication cost are optimized simultaneously. The federated recommendation system (FedRS), which is the applicatio...

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Vydáno v:Expert systems with applications Ročník 201; s. 116963
Hlavní autoři: Cui, Zhihua, Wen, Jie, Lan, Yang, Zhang, Zhixia, Cai, Jianghui
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.09.2022
Elsevier BV
Témata:
Communication
Evolutionary algorithms
Federated learning
Federated recommendation model
Genetic algorithms
Many-objective evolutionary algorithm
Mathematical models
Optimization
Performance evaluation
Process parameters
Recommendation system
Recommender systems
RVEA
Many-objective evolutionary algorithm
Federated learning
RVEA
Federated recommendation model
Recommendation system
ISSN:0957-4174, 1873-6793
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Shrnutí:•A novel communication-efficient federated recommendation model is proposed.•A many-objective evolutionary method is used to achieve parameter reduction.•Recommended performances and communication cost are optimized simultaneously. The federated recommendation system (FedRS), which is the application of the recommendation system (RS) in federated learning, has been creatively developed as increasing attention has been paid to user privacy protection. However, traditional federated learning consumes excessive communication time and resource, which seriously limits the development of the FedRS. To reduce the communication cost and improve the recommendation performance of FedRS, an improved many-objective federated recommendation model with a novel parameter reduction strategy is proposed in this paper. The model aims to optimize the number of parameters shared between the server and client to improve communication efficiency in the federated process. Optimal parameter selection solutions can be obtained using the many-objective evolutionary algorithm (MaOEA), which can optimize the recommendation accuracy, novelty, diversity, and communication efficiency of FedRS simultaneously. Furthermore, the reference vector guided evolutionary algorithm (RVEA) was adopted to evaluate the proposed model. Experiments were performed on two famous recommendation datasets to examine the superiority of RVEA for the evaluation. The performance results indicated that the proposed model can not only provide accurate, diverse, and novel recommendations for the client, but can also realize efficient communication.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.116963