Combating Quality Distortion in Federated Learning with Collaborative Data Selection

Federated Learning (FL), a paradigm facilitating collaborative model training across distributed devices, has attracted substantial attention due to its potential to address privacy concerns and data localization requirements. However, the inherent inaccessibility of data poses a critical challenge in ensuring data quality within FL systems. Consequently, FL systems grapple with a range of data-related issues, encompassing erroneous samples, imbalanced data distributions, and data skew, all of which impose a significant impact on model performance. Therefore, the judicious selection of appropriate data for training is of paramount importance as it seeks to ameliorate these challenges. This research paper tackles a crucial but often overlooked concern: the presence of low-quality data samples. In such circumstances, we introduce an innovative algorithm that strategically curates a subset of data for each training iteration, with the overarching objective of optimizing the model’s accuracy while simultaneously addressing privacy concerns and reducing communication costs. Our primary innovation lies in the global selection of data, in contrast to the conventional approach that relies on individualized, client-specific data selection. Furthermore, we introduce a novel medical dataset tailored specifically for classification tasks. This dataset intentionally incorporates various attributes associated with low-quality data to effectively replicate real-world conditions. Through rigorous empirical evaluation, we show the effectiveness of our algorithm using this dataset. The results demonstrate a notable improvement of approximately 2–3% in model performance, particularly in scenarios characterized by imbalanced data distributions.