Decentralized Topology

Decentralized topology in federated learning aims to improve robustness and privacy by removing the central server, a single point of failure in traditional federated learning. Current research focuses on developing efficient algorithms, such as dynamic average consensus methods, to coordinate model updates among distributed agents while preserving data privacy and ensuring convergence to a globally consistent model. This shift towards decentralized architectures is significant because it enhances the security and scalability of federated learning, enabling its application in sensitive data domains and large-scale distributed systems.