In today's fast-paced world of stock exchange, staying ahead of the game is crucial for traders and researchers alike. Artificial intelligence (AI) has the potential to revolutionize the industry, but its implementation has been hindered by a number of challenges, including data privacy and access to large and diverse datasets. However, a new technology called federated learning is poised to change all of that. Federated learning is a decentralized machine learning procedure that allows multiple data providers to train machine learning models without pooling their data. Instead, the data remains locked on servers and only the predictive models travel between the servers. This approach not only respects data ownership and privacy, but it also allows each participant to benefit from a larger pool of data, resulting in increased machine learning performance. Federated Learning Brings AI to Stock Exchange The AISHE system is a prime example of how federated learning can be applied to...