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pFedCal: Lightweight Personalized Federated Learning With Adaptive Calibration Strategy

Year: 2025

Type: article

Source: IEEE Transactions on Services Computing

DOI: 10.1109/tsc.2025.3553707

Authors: Dongshang Deng, Xuangou Wu, Tao Zhang, Chaocan Xiang, Wei Zhao, Minrui Xu, Jiawen Kang, Zhu Han, Dusit Niyato

Institutions: Chongqing University, Anhui University of Technology, Beijing Jiaotong University, Nanyang Technological University, Guangdong University of Technology, University of Houston

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funders: National Science Foundation, Ministry of Education - Singapore, National Natural Science Foundation of China, China Postdoctoral Science Foundation

Awards: G2850947191, G4228552076, G6186783524, G6372342641, G6471033387, G6905679517, G692032742, FW-HTF-R/Collaborative Research: FAIR4WISE: Future AI and Robotics for Women in Smart Engineering, G8119772024, G8567757829, Excellence in Research: Research Capacity and Partnerships Building in Next-Generation Communication Ecosystems with Vertical Intelligence

OpenAlex ID: W4408792324

FWCI: 2.04

Cites: 41

Cited by: 5

Related to: 10