Federated learning is a machine learning approach where models are trained collaboratively across many devices or organizations without centralizing raw data. Instead of sending data to a server, each participant trains a local model and shares only model updates, such as gradients or weights. A central coordinator aggregates these updates to improve a global model.

This paradigm addresses privacy, security and regulatory concerns in data intensive domains such as healthcare, finance and mobile applications. Sensitive information can remain on hospitals’ servers or users’ phones while still contributing to a shared model. Techniques such as secure aggregation, differential privacy and homomorphic encryption are used to reduce the risk that model updates leak private information.

Research has focused on handling challenges unique to federated settings. Data are often non independent and identically distributed across clients, leading to biased local training and slower convergence. Participants can have highly variable computing power, communication bandwidth and availability. Algorithms like Federated Averaging and its variants have been developed to cope with heterogeneous data, stragglers and limited communication by using local multiple epoch training and compression of updates.

Robustness and fairness are active research areas. Adversarial or unreliable clients can poison the model, so aggregation rules and anomaly detection methods are explored to increase resilience. Fairness aims to ensure that performance does not degrade for underrepresented clients or subpopulations.

Applications span keyboard prediction, medical imaging, medical records, recommendation systems and industrial monitoring. Current work is moving toward scalable, cross silo and cross device deployments, better personalization of global models to local users and tighter integration of privacy guarantees with practical performance.