Auto-FL-Research deploys constrained coding agents to search federated learning recipes and reports mixed gains on four of five FLamby healthcare tasks and five of six LEAF profiles after five-seed repeats and same-budget controls.
FedNAS: Federated deep learning via neural architecture search
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DeepFedNAS delivers up to 1.21% higher accuracy and 61x faster architecture search for federated learning on heterogeneous IoT by replacing random supernet sampling with Pareto-optimal elite architectures and using a multi-objective fitness function as a zero-cost proxy.
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Auto-FL-Research: Agentic Search for Federated Learning Algorithms
Auto-FL-Research deploys constrained coding agents to search federated learning recipes and reports mixed gains on four of five FLamby healthcare tasks and five of six LEAF profiles after five-seed repeats and same-budget controls.