👉 Holders computing, also known as distributed or federated learning, is a decentralized machine learning approach where multiple participants, referred to as holders, contribute their local data and computational resources to train a shared model without exchanging the actual data itself. Each holder trains a local model on their private dataset and then shares only the model updates (e.g., gradients or weights) with a central server or among themselves, which aggregates these updates to improve the global model. This method enhances privacy and security by keeping sensitive data on local devices, reduces the need for extensive data transfer, and can be particularly beneficial when dealing with large-scale, distributed datasets where centralizing the data is impractical or infeasible. Holders computing leverages the collective power of distributed resources to build robust models while preserving data confidentiality and minimizing communication costs.
