Federated Learning or Federated machine learning (FML), a type of machine learning, allows multiple parties to train a single model while protecting the privacy of their respective data. In classical machine learning, data collection and storage are often centralized, that is, they happen all at once. On the other hand, when utilizing FML, the data is stored on the computers or servers of the involved parties, and the model is jointly trained without the need for raw data. In FML, a local model is trained on the data of each party, and the local models are combined to form a global model. This procedure can be repeated in order to improve the global model's accuracy. The main advantage of FML is that it protects customer privacy while allowing businesses to benefit from group intelligence. Applications of Federated Learning: Numerous practical applications of FML can be found in the banking, healthcare, and smart city industries. In the healthcare sector, for example, multiple organizations can collaborate to train a model that can detect illnesses without revealing their patients' private health information. Financial institutions can collaborate to protect the privacy of their customers' transaction data while training a fraud detection model. Since FML is a rapidly evolving field, there are a number of problems that need to be resolved, such as model security, efficient communication, and heterogeneous data. How does Federated Machine Learning (FML) work? A subset of machine learning called federated machine learning allows multiple parties to collaborate on creating a machine learning model without disclosing any of their individual data. Instead of collecting data from all participants and centralizing it in one location, federated machine learning allows each person to maintain their data locally and train a model collectively by sharing only model updates rather than raw data. Below is a summary of how federated machine learning works: Data distribution: With their datasets, the participants hope to develop a machine-learning model. Subsets of the datasets are separated out and kept private for training purposes. Initialization of the model: The central server sends the initialized version of a machine learning model (e.g., neural network to each participant. Local training: To train the model, each participant uses their own local dataset. The local dataset is the subset of the data that the model sees during training. The model generates an update with parameters that can be adjusted in order to enhance the model's fit to the local dataset. Model aggregation: Each participant's changes are sent to the central server, which combines them to create a new model. The aggregation method is commonly used to compute the average of the updates, where weights are determined by the number of data points in each participant's local dataset. Model distribution: After receiving the updated model, each participant applies it