FNRE: A Novel Approach to Heterogeneous Label Noise Rates Estimation in Federated Learning
Qian Rong1, Lu Zhang2, Ling Yuan1,*, Zhong Yang3, Guohui Li3
1 School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
2 School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
3 School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China
* Corresponding Author: Ling Yuan. Email: 
# Supported by the National Natural Science Foundation of China under Grant Nos. 62272180 and 62272176. The computation is completed in the HPC Platform of Huazhong University of Science and Technology
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.075102
Received 24 October 2025; Accepted 03 March 2026; Published online 08 April 2026
Abstract
Federated learning (FL) enables collaborative model training across decentralized clients without sharing raw data, thereby preserving privacy. However, in real-world FL deployments—such as sensor-based activity recognition, wearable health monitoring, and industrial Internet of Things, where local training data often suffer from heterogeneous noisy labels due to diverse collection environments, sensor limitations, and labeling errors. These noisy labels, typically distributed unevenly across clients due to differences in client-side annotation, exacerbate Non-Independent and Identically Distributed (non-IID) data issues, leading to biased updates, unstable convergence, and degraded global model performance. Accurate estimation of client-specific noise rates is therefore crucial for adaptive algorithm selection, personalized parameter tuning, noise-aware aggregation, and resource allocation in FL. Existing noise rate estimation methods, primarily developed for centralized settings, require client-specific clean validation sets or prior knowledge of noise, making them impractical in privacy-sensitive federated settings. In this work, we propose a federated noise rate estimation (FNRE) method that eliminates the need for per-client clean datasets or prior knowledge of noise. Our approach requires only a minimal assumption—at least one client with a small clean validation set—and leverages the global model’s predictions to estimate local noise rates across all clients. Specifically, the method computes global prediction accuracy using data from the small, clean subset of clients, broadcasts this accuracy to all participants, and enables each client to infer its noise rate using its own annotated labels and the predicted label sequence. We further provide a theoretical analysis with provable error bounds. Extensive experiments on image classification (CIFAR-10, CIFAR-100) and sensor-based activity recognition (Widar, WISDM-W) under various synthetic and real-world noisy label settings demonstrate that our method achieves a noise rate estimation Mean Absolute Error (MAE) of only 0.82%–2.19%, outperforming state-of-the-art baselines by 29.8%–49.9% on average while maintaining practicality in privacy-sensitive federated environments.
Keywords
Noise rate estimation; noisy labels; federated learning; sensor-based activity recognition
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