FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework

Documenti analoghi

• Masked Autoencoders are Parameter-Efficient Federated Continual Learners
  di: He, Yuchen, et al.
  Pubblicazione: (2024)
• Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data
  di: Qiao, Yu, et al.
  Pubblicazione: (2024)
• One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity
  di: Wang, Naibo, et al.
  Pubblicazione: (2024)
• Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data
  di: Ardıç, Emre, et al.
  Pubblicazione: (2026)
• Studying Various Activation Functions and Non-IID Data for Machine Learning Model Robustness
  di: Dang, Long, et al.
  Pubblicazione: (2025)