Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning 事件

Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning arXiv:2606.10250v1 Announce Type: cross 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