Description: Federated Averaging is a key algorithm in the field of federated learning, enabling the creation of artificial intelligence models without the need to centralize data. In this approach, models are trained locally on distributed devices, such as smartphones or IoT sensors, where data is generated. Each device adjusts its local model using its own data, and then, instead of sending the data to a central server, it sends only the trained model parameters. These parameters are averaged on the server to create a global model that reflects the accumulated knowledge of all participating devices. This method not only preserves data privacy but also reduces latency and bandwidth required for data transmission. Furthermore, Federated Averaging allows models to adapt to the specifics of local data, thereby improving their performance on specific tasks. This approach is particularly relevant in a world where data privacy is an increasing concern and where edge processing capability is becoming increasingly important for real-time applications.
History: The concept of federated learning was first introduced by Google in 2017 in a paper describing how machine learning models could be trained on distributed devices without compromising user data privacy. Since then, Federated Averaging has evolved as a central technique in this field, enabling collaboration among multiple devices to improve model accuracy without the need to share sensitive data.
Uses: Federated Averaging is primarily used in applications where data privacy is critical, such as in the healthcare sector, where medical devices can train diagnostic models without sharing patient data. It is also applied in improving prediction models on various distributed devices, such as in personalizing recommendations in a wide range of applications.
Examples: A practical example of Federated Averaging is the text prediction system on distributed devices, where the model is trained locally on each device and updated on the central server without sending the user’s input data. Another case is the training of fraud detection models in banking applications, where sensitive user data never leaves their devices.
