Benchmark for Personalized Federated Learning

• Published in: IEEE open journal of the Computer Society Vol. 5; pp. 1 - 12
• Main Authors: Matsuda, Koji, Sasaki, Yuya, Xiao, Chuan, Onizuka, Makoto
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Benchmark testing; Benchmarking; Benchmarks; Clients; Computational modeling; Computer vision; Customization; Data models; Datasets; Distributed Computing; Federated Learning; Heterogeneity; Machine learning; Natural language processing; Servers; Task analysis; Training

Federated learning is a distributed machine learning approach that allows a single server to collaboratively build machine learning models with multiple clients without sharing datasets. Since data distributions may differ across clients, data heterogeneity is a challenging issue in federated learni...