FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization

Zhen WANG, Weirui Kuang, Ce Zhang, Bolin Ding, Yaliang Li

Keywords: federated learning, hyperparameter optimization

Abstract: Hyperparameter optimization (HPO) is crucial for machine learning algorithms to achieve satisfactory performance. Its research progress has been boosted by existing HPO benchmarks. Nonetheless, existing efforts in benchmarking all focus on HPO for traditional centralized learning while ignoring federated learning (FL), a promising paradigm for collaboratively learning models from dispersed data. In this paper, we first identify some uniqueness of HPO for FL algorithms from various aspects. Due to this uniqueness, existing HPO benchmarks no longer satisfy the need to compare HPO methods in the FL setting. To facilitate the research of HPO in the FL setting, we propose and implement a benchmark suite FedHPO-Bench that incorporates comprehensive FedHPO problems, enables flexible customization of the function evaluations, and eases continuing extensions. We also conduct extensive experiments based on FedHPO-Bench to provide the community with more insights into FedHPO. We open-sourced FedHPO-Bench at https://github.com/FedHPO-Bench/FedHPO-Bench-ICLR23.

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