👉 Conf computing, or Confederated Federated Learning, is a decentralized machine learning approach that enables multiple entities, such as organizations or devices, to collaboratively train a shared model while keeping their data localized and private. Instead of centralizing data on a single server, Conf computing allows each participant to train a model on their own dataset using local data and then shares only the model updates (e.g., gradients or weights) with a central server, which aggregates these updates to improve the global model. This method enhances data privacy and security by preventing sensitive information from being exposed, making it ideal for applications involving regulated industries like healthcare or finance. Conf computing also reduces data transfer costs and bandwidth usage, as only model parameters are shared rather than raw data. By fostering collaboration without compromising confidentiality, Conf computing represents a significant advancement in distributed AI, enabling scalable and privacy-preserving machine learning across diverse domains.
