LLMs can act as adaptive meta-aggregators in federated learning without server-side training, dynamically selecting aggregation strategies based on weight analysis—though performance depends heavily on proxy feedback and the approach doesn't yet match specialized methods like SCAFFOLD.
FedReason uses a large language model to dynamically choose aggregation rules for federated learning by analyzing client model weights and testing candidates on a small proxy dataset. The LLM runs code in a sandbox to inspect weights, selects or writes Python aggregation programs, and the server applies them until the next LLM call, falling back to FedAvg if invalid.