Auto-FL-Research: Agentic Search for Federated Learning Algorithms

Researchers have introduced Auto-FL-Research, an automated framework designed to search for and optimize the various algorithmic settings required for federated learning. By using an agentic approach to navigate complex choices like server aggregation rules and local training schedules, the system aims to reduce the manual labor typically required to refine decentralized machine learning models.