Auto-FL-Research tackles the many design choices that shape federated learning algorithms, including optimizers, aggregation rules, schedules, regularization, and architectures. The paper frames those choices as a search problem that agents can explore.
The work is relevant because federated learning experiments can be expensive and brittle. Agentic search could help researchers test more combinations while keeping track of tradeoffs across privacy, performance, and deployment constraints.