👉 Drops computing, also known as Drops or Drop Computing, is a distributed machine learning technique that leverages a network of edge devices to perform computations closer to where the data is generated, rather than relying on centralized cloud servers. This approach significantly reduces latency and bandwidth usage, making it particularly suitable for real-time applications such as autonomous vehicles or smart cities. Drops computing involves deploying lightweight models on edge devices that can process data locally, and these devices communicate with each other or a central server to aggregate results and update the global model. By minimizing the amount of data that needs to be transmitted, Drops computing enhances privacy and efficiency while maintaining high performance.
