As devices around us become smarter, the demand to train machine learning models on the data they collect grows. However, transferring all the data to a central server poses privacy risks and bandwidth challenges. Federated Learning addresses these issues by training models locally on each device and only sharing model updates, thereby keeping raw data decentralized and preserving user privacy.
Federated Learning is a machine learning paradigm where the training process is distributed across multiple devices or nodes. These nodes compute model updates locally on their data and send these updates to a central server. The server aggregates these updates, refines the global model, and then sends it back to the nodes. This process ensures that raw data never leaves the local device, enhancing user privacy and reducing data transfer costs.
Unlike traditional machine learning, where all data is pooled at a central server for training, FL keeps the data local. In FL, only model updates, like gradients or weights, are transmitted to the central server. This decentralized approach significantly reduces the risk of data breaches, provides better data privacy, and accommodates devices with limited bandwidth or sporadic connectivity.
Federated Learning offers several benefits:
While FL is promising, it comes with challenges: