FedSyn: Synthetic Data Generation using Federated Learning (2203.05931v2)

Abstract: As Deep Learning algorithms continue to evolve and become more sophisticated, they require massive datasets for model training and efficacy of models. Some of those data requirements can be met with the help of existing datasets within the organizations. Current Machine Learning practices can be leveraged to generate synthetic data from an existing dataset. Further, it is well established that diversity in generated synthetic data relies on (and is perhaps limited by) statistical properties of available dataset within a single organization or entity. The more diverse an existing dataset is, the more expressive and generic synthetic data can be. However, given the scarcity of underlying data, it is challenging to collate big data in one organization. The diverse, non-overlapping dataset across distinct organizations provides an opportunity for them to contribute their limited distinct data to a larger pool that can be leveraged to further synthesize. Unfortunately, this raises data privacy concerns that some institutions may not be comfortable with. This paper proposes a novel approach to generate synthetic data - FedSyn. FedSyn is a collaborative, privacy preserving approach to generate synthetic data among multiple participants in a federated and collaborative network. FedSyn creates a synthetic data generation model, which can generate synthetic data consisting of statistical distribution of almost all the participants in the network. FedSyn does not require access to the data of an individual participant, hence protecting the privacy of participant's data. The proposed technique in this paper leverages federated machine learning and generative adversarial network (GAN) as neural network architecture for synthetic data generation. The proposed method can be extended to many machine learning problem classes in finance, health, governance, technology and many more.

FedSyn: Employing Federated Learning for Synthetic Data Generation

Introduction to FedSyn

The proliferation of Deep Learning (DL) applications across various sectors necessitates vast amounts of data for model training, which often poses significant challenges due to data privacy issues, scarcity, and inherent biases. A promising approach to mitigate these challenges is synthetic data generation. This approach, however, introduces its set of problems, notably the privacy concerns when dealing with data from multiple sources or organizations. In this context, the paper introduces FedSyn, a novel framework leveraging Federated Learning (FL) for synthetic data generation in a privacy-preserving and collaborative manner.

Federated Learning and Synthetic Data Generation

FedSyn amalgamates Federated Learning, Generative Adversarial Networks (GANs), and differential privacy to address the core issues of data scarcity, bias, and privacy. The framework enables the generation of diverse synthetic datasets that maintain the statistical distribution of the original data across distinct entities, without requiring access to any actual data points of the participating organizations. This model is particularly vital for sensitive domains like finance, healthcare, and IoT, where data sharing poses substantial privacy and legal challenges.

Core Components and Methodology

The FedSyn framework comprises three primary components: GAN for data generation at the participant level, Laplacian noise for differential privacy, and a Federated Learning protocol for collaborative model training. Let's unpack these components further:

• Generative Adversarial Network (GAN): Each participant independently employs a GAN for synthetic data generation at the local level. The network architecture consists of a generator and a discriminator, tasked with generating new, synthetic data points that are indistinguishable from real data to the discriminator.
• Differential Privacy: By adding Laplacian noise to the local model parameters before their aggregation, FedSyn ensures that the privacy of the participating entities' data is preserved. This step is crucial in maintaining the confidentiality of the underlying data when model parameters are shared across the network.
• Federated Learning: This protocol orchestrates model training across various participants without requiring them to share their actual data. Model parameters are aggregated from all participants, enhancing the model with a broader view of the data landscape across different organizational datasets.

Implications and Future Prospects

The proposed FedSyn framework not only addresses the problem of data scarcity and bias but also ensures that privacy is upheld, enabling organizations to leverage collective data insights without compromising data security. While FedSyn has demonstrated promising results, specifically in its ability to generate high-quality synthetic data across participating entities, several avenues remain open for further research. This includes exploring the impact of different differential privacy techniques on model accuracy and the computational efficiency of federated learning models. Additionally, understanding the energy consumption implications of federated versus centralized models can pave the way for more sustainable machine learning practices.

Conclusion

FedSyn presents a compelling approach to synthetic data generation using federated learning, addressing the paramount issues of data privacy, scarcity, and bias. By leveraging the collective power of data across diverse entities while ensuring stringent privacy safeguards, FedSyn sets a foundation for future research and development in privacy-preserving synthetic data generation. As the demand for extensive datasets in deep learning continues to surge, frameworks like FedSyn will play a critical role in enabling collaborative and ethical AI development across industries.