👉 Invitation computing, also known as invitation-based or federated learning, is a distributed machine learning technique that allows multiple parties to collaboratively train a model without sharing their raw data. In this approach, each participant (often referred to as an "invitee") holds a portion of the dataset and contributes to the model's learning process by sharing only model updates, such as gradients or weights, rather than the actual data. These updates are aggregated by a central server to improve the global model, ensuring privacy and security since sensitive data remains on the local devices. This method is particularly useful in scenarios where data privacy, regulatory compliance, or resource constraints prevent centralized data storage and processing. Invitation computing enables efficient model training across decentralized environments, such as mobile devices, healthcare systems, or IoT networks, fostering collaboration while maintaining data confidentiality.
