Fed-Focal Loss for Imbalanced Data Classification in Federated Learning
International Workshop on Federated Learning for User Privacy and Data Confidentiality (FL-IJCAI'20)
Abstract
This paper extends the Focal Loss function used in image detectors to Federated Learning along with a tunable sampling framework for solving the class imbalance problem. The approach reshapes cross-entropy loss to down-weight the loss assigned to well-classified examples, following focal loss principles. Additionally, it leverages a tunable sampling framework to account for selective client model contributions on the central server, improving detector focus during training and enhancing robustness.
Presentation Details
This talk is part of Technical Talks Session 2 at the FL-IJCAI’20 workshop. The presentation will cover:
- Extension of Focal Loss to Federated Learning context
- Implementation of Fed-Focal loss function
- Tunable sampling framework for client selection
- Experimental results across multiple datasets
- Impact on training stability and model robustness
Reviews
The paper received positive reviews highlighting:
- Novel application of Focal Loss in Federated Learning context
- Comprehensive experimental evaluation
- Promising results in terms of accuracy and robustness
- Well-written presentation of the methodology
Workshop Information
The International Workshop on Federated Learning for User Privacy and Data Confidentiality (FL-IJCAI’20) focuses on machine learning systems adhering to privacy-preserving and security principles. The workshop provides a forum to discuss open problems and share ground-breaking work in secure and privacy-preserving compliant machine learning.