👉 Fed Computing is a decentralized, open-source framework designed to enable secure, privacy-preserving, and collaborative machine learning across multiple parties without sharing raw data. It leverages blockchain technology to create a trustless environment where participants (or "nodes") contribute computational resources, data, or model updates in a federated manner. Each node trains a local model on its own dataset, and only aggregated model updates—rather than the data itself—are shared with a central server or a distributed network, which then combines these updates to improve a global model. This approach ensures data privacy, as sensitive information remains localized, and fosters collaboration among entities (e.g., hospitals, companies) that might otherwise hesitate to share data due to privacy or regulatory concerns. Fed Computing emphasizes transparency, security, and efficiency, making it particularly valuable for applications like healthcare analytics, where data confidentiality is critical.
