New Federated Learning Method Addresses Class Imbalance Across Distributed Data
Researchers have proposed FedBB, a federated learning approach that tackles class imbalance problems across three levels: within individual classes, between classes, and across different client devices. The method uses a specialized loss function and client reweighting strategy to improve model performance when training data is unevenly distributed. This addresses a fundamental challenge in federated learning where data across multiple devices is non-identically distributed, which degrades model accuracy.
A new paper on arXiv presents FedBB, a federated learning framework designed to handle class imbalance—a persistent problem where some data categories are underrepresented compared to others. The researchers identify and analyze imbalance at three distinct levels: inter-case (imbalance within a single class), inter-class (differences in the number of samples between classes), and inter-client (varying data distributions across different participating devices). FedBB comprises two components: a Positive Negative Balanced (PNB) loss function that weights minority cases and classes more heavily during local training, and a Client Balanced Reweighting (CBR) mechanism that adjusts how much each client's trained model contributes to the global model during aggregation. Experiments on X-ray and natural image datasets show FedBB outperforms existing algorithms while requiring minimal statistical information sharing, which benefits privacy protection. The authors demonstrate through ablation studies that both components independently contribute to performance improvements.
What's missing
The paper does not discuss computational overhead or communication costs compared to baseline methods, which are important practical considerations in federated learning deployments. Additionally, the study's limitations regarding scalability to very large numbers of clients or extremely sparse data scenarios are not explicitly addressed in the abstract.
What different sources said
- arXiv cs.LGCenter
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
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