Sharing Models Without Sharing Data: A Review of Federated Learning Approaches

Federated learning is a revolutionary approach to machine learning that allows multiple parties to collaboratively train a model without sharing their data. In this survey, we explore the challenges and opportunities of federated learning, specifically in the context of sharing models without sharing data. We discuss three key scenarios where this is particularly relevant: (1) when sharing data is not an option, (2) when there are concerns about data privacy and security, and (3) when there are issues with data heterogeneity.
To address these challenges, we propose several strategies for sharing models without sharing data, including the use of neural networks, aggregation methods, and distributed computing techniques. We also discuss the importance of evaluating the performance of federated learning algorithms on real-world datasets to ensure their effectiveness in practical applications.
One of the key insights from our survey is that federated learning can help address the problem of data scarcity by allowing multiple parties to contribute their data without having to share it with others. This can be particularly useful in scenarios where data is expensive or difficult to obtain, such as in medical research or financial services.
Another important finding is that federated learning can help improve data privacy and security by allowing data owners to maintain control over their data while still benefiting from the insights of machine learning. This is particularly relevant in applications such as healthcare, where patient data must be kept confidential.
Finally, our survey highlights the need for further research on federated learning to better understand its scalability and robustness in real-world scenarios. While federated learning has shown promising results in small-scale experiments, it remains to be seen how well it will perform in large-scale applications with complex data distributions.
In summary, this survey provides a comprehensive overview of the challenges and opportunities of federated learning in sharing models without sharing data. We discuss the key scenarios where this is relevant, propose several strategies for addressing these challenges, and highlight the need for further research to fully realize the potential of federated learning in real-world applications.