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FLEX: Robust client selection for dynamic federated learning environments
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AI
DOI：10.1016/j.knosys.2026.116014.png)
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Abstract
En 中文
• FLEX: Novel client selection for dynamic FL via change detection and adaptive restart. • Page-Hinckley framework monitors client contributions with provable detection guarantees. • Adaptive threshold improves late-stage detection sensitivity by 50%–200% efficiently. • Achieves 94.2% performance retention and 2–3 × faster convergence than baselines.
Keywords:
Federated Learning
Client Selection
Change Detection
Adaptive Restart
Dynamic Environments
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
ShareSaveShareSaveFELACS: Federated learning with adaptive client selection for IoT DDoS attack detection.png)
Computers & Security
OAAI
Computers & SecurityIF5.4
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