👉 Compromise computing, also known as edge or federated learning, is a distributed computing paradigm that balances the need for data privacy and security with the benefits of centralized machine learning. In this approach, data remains on local devices or edge servers rather than being transferred to a central server. Instead, only model updates or aggregated insights are shared, reducing the risk of data breaches and ensuring compliance with privacy regulations. This method allows for collaborative model training across multiple devices or organizations while keeping sensitive data localized, making it particularly useful in scenarios where privacy is paramount, such as healthcare or finance. By optimizing the trade-off between data utility and privacy, compromise computing enables efficient and secure machine learning applications.
