👉 Seeds computing is a novel approach to distributed machine learning that leverages the collective computational power of a network of devices, such as smartphones or IoT sensors, to train machine learning models. Instead of relying on centralized servers, seeds computing distributes the training process across these edge devices, each of which holds a small, randomly selected subset of the data (the "seed"). These seeds perform local computations, updating their own model parameters based on the data they receive from other seeds and aggregating these updates periodically with a central server or through peer-to-peer communication. This decentralized method not only enhances privacy and reduces latency but also enables the training of models on massive datasets that would be impractical or impossible for a single entity to handle. By harnessing the combined resources of many devices, seeds computing can achieve high scalability and efficiency, making it particularly suitable for applications requiring real-time processing or operating in environments with limited connectivity.
