FedNoisy: Federated Noisy Label Learning Benchmark
This work addresses the problem of noisy label learning in federated settings for researchers, providing a benchmark to facilitate method development, though it is incremental as it builds on existing efforts in noisy label defense.
The authors tackled the lack of a standardized benchmark for studying noisy labels in federated learning by introducing FedNoisy, which includes 20 basic settings across 6 datasets and a simulation pipeline with 9 baselines, enabling comprehensive exploration and comparison.
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients. But meanwhile, the distributed and isolated nature of data isolation may be complicated by data quality, making it more vulnerable to noisy labels. Many efforts exist to defend against the negative impacts of noisy labels in centralized or federated settings. However, there is a lack of a benchmark that comprehensively considers the impact of noisy labels in a wide variety of typical FL settings. In this work, we serve the first standardized benchmark that can help researchers fully explore potential federated noisy settings. Also, we conduct comprehensive experiments to explore the characteristics of these data settings and the comparison across baselines, which may guide method development in the future. We highlight the 20 basic settings for 6 datasets proposed in our benchmark and standardized simulation pipeline for federated noisy label learning, including implementations of 9 baselines. We hope this benchmark can facilitate idea verification in federated learning with noisy labels. \texttt{FedNoisy} is available at \codeword{https://github.com/SMILELab-FL/FedNoisy}.