Fairness-guided federated training for generalization and personalization in cross-silo federated learning

Ruipeng ZHANG; Ziqing FAN; Jiangchao YAO; Ya ZHANG; Yanfeng WANG

doi:10.1631/FITEE.2400279

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Fairness-guided federated training for generalization and personalization in cross-silo federated learning

Regular Papers | Updated：2025-03-13

- Fairness-guided federated training for generalization and personalization in cross-silo federated learning
  Enhanced Publication
- 面向跨中心联邦学习的泛化与个性化兼顾的公平性引导联邦训练
- “In the field of artificial intelligence, the paper introduces its research progress in cross-silo federated learning. Expert xx established the fairness-guided federated training for generalization and personalization (FFT-GP) system, which provides solutions to solve the data heterogeneity issue and balance personalization and generalization in federated learning.”
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 1, Pages: 42-61(2025)
- Affiliations：
  
  1.School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai 200240, China
  2.Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai 200240, China
  3.Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
- Author bio：
  
  ‡Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2400279
  CLC： TP391.4
- Received：12 April 2024，
  
  Revised：14 May 2024，
  
  Published Online：16 December 2024，
  
  Published：2025-01
- Accepted：
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Ruipeng ZHANG, Ziqing FAN, Jiangchao YAO, et al. Fairness-guided federated training for generalization and personalization in cross-silo federated learning[J]. Frontiers of information technology & electronic engineering, 2025, 26(1): 42-61.
DOI：

Ruipeng ZHANG, Ziqing FAN, Jiangchao YAO, et al. Fairness-guided federated training for generalization and personalization in cross-silo federated learning[J]. Frontiers of information technology & electronic engineering, 2025, 26(1): 42-61. DOI： 10.1631/FITEE.2400279.

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Fairness-guided federated training for generalization and personalization in cross-silo federated learning

DOI：10.1631/FITEE.2400279