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thenewstack.io·16h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
TL;DR
Class imbalance in federated learning (FL) leads to non-identically distributed data, negatively impacting model performance. To address this, FedBB was developed, incorporating a Positive Negative Balanced (PNB) loss function and Client Balanced Reweighting (CBR) to enhance training and aggregation.
✦ Why It Matters
Engineers can implement FedBB to improve model performance in federated learning scenarios with imbalanced data.
Key Takeaways
- Utilize FedBB to address class imbalance in federated learning.
- Apply PNB loss to enhance local training on skewed datasets.
- Incorporate CBR for effective model aggregation across clients.
- Conduct experiments to validate performance improvements with FedBB.
How It Works
FedBB operates by defining three levels of imbalance: inter-case, inter-class, and inter-client. The PNB loss function optimizes local training by emphasizing minority classes, while CBR adjusts client contributions during model aggregation based on data distribution.
This dual approach ensures that the global model is robust against imbalances.