A local model in federated learning refers to the machine learning model that is trained independently on a single device or server (referred to as a “client”) using its own data. Unlike traditional centralized training, where all data is pooled into one location, federated learning keeps data decentralized. Each client trains its local model using its private dataset, and only the model updates (e.g., gradients or weights) are shared with a central server. This approach preserves data privacy, as raw data never leaves the client. The local model is a core component of the federated learning process, enabling collaborative training without direct data sharing.
The training process for a local model typically follows these steps: First, the central server initializes a global model and sends it to all clients. Each client then trains this model using its local data, adjusting parameters to minimize prediction errors on its specific dataset. For example, a smartphone might train a local model to improve keyboard autocomplete suggestions based on the user’s typing history. After training, the client sends the updated model parameters (not the data) back to the server. The server aggregates these updates—often by averaging them—to create a new global model, which is redistributed to clients for further rounds of training. This cycle repeats until the global model achieves satisfactory performance.
Developers implementing federated learning must design local models to operate efficiently on diverse hardware, handle varying data distributions, and manage communication with the server. For instance, a healthcare application might involve hospitals training local models on patient records, ensuring sensitive data remains on-premises. Frameworks like TensorFlow Federated or PyTorch provide tools to streamline client-side training loops and parameter aggregation. Challenges include handling non-IID (non-independent and identically distributed) data across clients and optimizing communication costs. By focusing on the local model’s role in privacy-focused, distributed training, developers can build systems that balance performance with data security.