Federated Learning

Federated learning allows multiple participants to collaborate on learning tasks without moving their raw data to a central server. Keeping data local reduces the direct exposure of sensitive information. However, this decentralized setting creates new challenges. Participants may have different data distributions, computational resources, and objectives. Communication may be limited, and the system must remain robust to noise, failures, or malicious behavior. These issues affect what can be learned and how efficiently it can be learned.