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Federated Learning (FL) enables collaborations among clients for trainmachine learning models while protecting their data privacy. Existing FLsimulation platforms that are designed from the perspectives of traditionaldistributed training, suffer from laborious code migration between simulationand production, low efficiency, low GPU utility, low scalability with highhardware requirements and difficulty of simulating stateful clients. In thiswork, we firstly demystify the challenges and bottlenecks of simulating FL, anddesign a new FL system named as FedML \texttt{Parrot}. It improves the trainingefficiency, remarkably relaxes the requirements on the hardware, and supportsefficient large-scale FL experiments with stateful clients by: (1) sequentialtraining clients on devices; (2) decomposing original aggregation into localand global aggregation on devices and server respectively; (3) scheduling tasksto mitigate straggler problems and enhance computing utility; (4) distributedclient state manager to support various FL algorithms. Besides, built upon ourgeneric APIs and communication interfaces, users can seamlessly transform thesimulation into the real-world deployment without modifying codes. We evaluate\texttt{Parrot} through extensive experiments for training diverse models onvarious FL datasets to demonstrate that \texttt{Parrot} can achieve simulatingover 1000 clients (stateful or stateless) with flexible GPU devices setting ($4\sim 32$) and high GPU utility, 1.2 $\sim$ 4 times faster than FedScale, and 10$\sim$ 100 times memory saving than FedML. And we verify that \texttt{Parrot}works well with homogeneous and heterogeneous devices in three differentclusters. Two FL algorithms with stateful clients and four algorithms withstateless clients are simulated to verify the wide adaptability of\texttt{Parrot} to different algorithms.

FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training