Mitigating the effects of non-IID data in federated learning with a self-adversarial balancing method

Federated learning (FL) allows multiple devices to jointly train a global model without sharing local data. One of its problems is dealing with unbalanced data. Hence, a novel technique, designed to deal with label-skewed non-IID data, using adversarial inputs is proposed. Application of the proposed algorithm results in faster, and more stable, global model performance at the beginning of the training. It also delivers better final accuracy and decreases the discrepancy between the performance of individual classes. Experimental results, obtained for MNIST, EMNIST, and CIFAR-10 datasets, are reported and analyzed.

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1. This work was supported by the Centre for Priority Research Area Artificial Intelligence and Robotics of Warsaw University of Technology within the Excellence Initiative: Research University (IDUB) programme and by the Laboratory of Bioinformatics and Computational Genomics and the High-Performance Computing Center of the Faculty of Mathematics and Information Science Warsaw University of Technology.

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2. Thematic Tracks Short Papers

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3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).