FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning

2026-08-24

AI Summary

为解决联邦学习中标签分布偏斜问题，提出FedCC算法，允许客户端将模糊样本标记为'未知'，并通过校准公共数据上的伪标签来平衡多数类和少数类的置信度。

Abstract

Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillation-based FL, each client applies its local model on an unlabeled public dataset, and shares only prediction results with the server. While heterogeneous local data introduces label distribution skew, thus biasing client models toward majority classes and leading to potentially inaccurate predictions. The lack of ground-truth labels in the public dataset hampers the server's ability to calibrate predictions, which ultimately degrades overall performance. To address this, we propose FedCC, a simple and effective algorithm for mitigating client misclassification. Instead of being forced to classify and risking error propagation, clients are allowed to tag ambiguous samples as 'unknown'. This additional class, together with calibrated pseudo-labels on the public data, balances confidence in majority classes against uncertainty in under-represented ones. Extensive experiments demonstrate that FedCC significantly outperforms existing methods, especially under severe label skew. In the extreme scenario where each client holds samples from only one of ten classes, FedCC achieves 67.3% accuracy, while baselines collapse to near-random results.

Problem

• Federated Learning
• Label Distribution Skew
• Distillation-Based FL
• Client Misclassification
• Public Dataset

Innovation

• Federated Learning
• Label Distribution Skew
• Pseudo-Labels
• Uncertainty