FedGP goes open source: reproducible evolutionary aggregation for federated learning with FASTER

Abstract Genetic Programming (GP) has recently been applied to evolve aggregation functions in Federated Learning (FL), instead of fixing them in advance. FedGP represents aggregation rules as symbolic expressions that evolve during training and has shown competitive performance under non-IID data. Progress on GP-based aggregation, however, has been constrained by two practical barriers: limited reproducibility in FL experimentation and the absence of a public FedGP implementation. This paper presents FASTER, an open-source platform that provides the first public implementation of FedGP and integrates it with standard baselines (FedAvg, weighted FedAvg, FedProx, and FedNova). FASTER provides a browser- and CLI-based configuration-driven workflow for controlled FL experiments with built-in or custom datasets, IID and non-IID partitions, client scheduling, churn simulation, live metric tracking, and structured artifact export. Each run is stored with its configuration, logs, metrics, metadata, checkpoints, and, for FedGP, the individuals evolved across generations. In a demonstration on FashionMNIST with non-IID data and client churn, FedGP outperforms all fixed baselines with statistically significant gains; GPU batch evaluation reduces the previously reported roughly tenfold wall-clock overhead to about 3.3× relative to fixed-rule methods. By placing evolved and fixed aggregation rules under identical controlled conditions, FASTER turns FedGP into a reusable, testable, and extensible research object for the evolutionary computation community.