👉 Lots computing is a decentralized machine learning technique that leverages the collective computational power of a network of devices, such as smartphones or IoT gadgets, to train machine learning models. Instead of relying on centralized servers, lots computing distributes the computational tasks across these devices, which perform local computations and share their results with a central server. This approach not only reduces the load on individual devices but also enhances privacy and security, as raw data remains on the user's device. The aggregated insights from these distributed computations are then used to improve the global machine learning model, making it more robust and adaptable to diverse data sets while minimizing latency and bandwidth usage.
