arXiv:2606.10250v1 Announce Type: new Abstract: Class imbalance is a common problem in deep learning that severely degrades performance. In federated learning (FL), it is a critical factor contributing to non-identically distributed data (non-IID). Building on several previous attempts, we define and analyze imbalance issues in FL at three levels: inter-case, inter-class, and inter-client. Inter-case imbalance addresses the imbalance in every single class; inter-class imbalance compares the number of data between different classes. Inter-client imbalance represents different skewness of local
This research addresses a fundamental challenge in federated learning, whose adoption is increasing as data privacy and distributed computation become more critical.
Improving Federated Learning's robustness to non-IID data enhances distributed AI capabilities, making it more practical for real-world, privacy-sensitive applications.
The ability to accurately diagnose and resolve data imbalance in federated learning will lead to more effective and deployable FL models across diverse datasets.
- · Federated Learning developers
- · Healthcare sector (for privacy-preserving AI)
- · Financial services (for secure data sharing)
- · Edge AI providers
- · Centralized deep learning approaches (in certain privacy-sensitive contexts)
Improved performance and broader adoption of federated learning solutions.
Increased ability for organizations to collaborate on AI model training without directly sharing sensitive raw data.
Acceleration of privacy-preserving AI innovation and new business models based on distributed intelligence.
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Read at arXiv cs.LG