  Federated Learning (FL) provides a privacy-preserving mechanism for
distributed training of machine learning models on networked devices (e.g.,
mobile devices, IoT edge nodes). It enables Artificial Intelligence (AI) at the
edge by creating models without sharing actual data across the network.
Existing research typically focuses on generic aspects of non-IID data and
heterogeneity in client's system characteristics, but they often neglect the
issue of insufficient data for model development, which can arise from uneven
class label distribution and highly variable data volumes across edge nodes. In
this work, we propose FLIGAN, a novel approach to address the issue of data
incompleteness in FL. First, we leverage Generative Adversarial Networks (GANs)
to adeptly capture complex data distributions and generate synthetic data that
closely resemble real-world data. Then, we use synthetic data to enhance the
robustness and completeness of datasets across nodes. Our methodology adheres
to FL's privacy requirements by generating synthetic data in a federated manner
without sharing the actual data in the process. We incorporate techniques such
as classwise sampling and node grouping, designed to improve the federated
GAN's performance, enabling the creation of high-quality synthetic datasets and
facilitating efficient FL training. Empirical results from our experiments
demonstrate that FLIGAN significantly improves model accuracy, especially in
scenarios with high class imbalances, achieving up to a 20% increase in model
accuracy over traditional FL baselines.
