FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization (2206.03966v4)

Abstract: Hyperparameter optimization (HPO) is crucial for machine learning algorithms to achieve satisfactory performance, whose progress has been boosted by related 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-B that incorporates comprehensive FL tasks, enables efficient function evaluations, and eases continuing extensions. We also conduct extensive experiments based on FedHPO-B to benchmark a few HPO methods. We open-source FedHPO-B at https://github.com/alibaba/FederatedScope/tree/master/benchmark/FedHPOB.

FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization

The paper "FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization" introduces a comprehensive benchmark suite designed to facilitate research in hyperparameter optimization (HPO) within federated learning (FL) frameworks. Traditional HPO benchmarks primarily focus on centralized learning paradigms, leaving a notable gap in resources suited for federated settings. This work addresses these limitations by offering a tailored benchmarking solution, FedHPO-B, which accounts for the unique challenges and opportunities posed by distributed data and participant heterogeneity in FL.

Key Contributions

1. Identification of Uniqueness in FedHPO: The paper begins by detailing how HPO in FL differentiates itself from centralized paradigms. This includes new hyperparameter dimensions prompted by FL's client-server architecture and the introduction of fidelity dimensions like sample rate, which balances client participation in each training round. The concurrent exploration of hyperparameters and one-shot optimization strategies are also highlighted as novel considerations within FL contexts.
2. Comprehensive Benchmark Suite: FedHPO-B includes a wide array of FL tasks spanning various data domains and model architectures. The benchmark incorporates tasks with CNN, BERT, GNN, LR, and MLP models across CV, NLP, graph, and tabular domains. This diversity is essential for evaluating the performance of HPO methods comprehensively and drawing unbiased conclusions.
3. Efficient Function Evaluation: Recognizing the computational expense of federated function evaluations, FedHPO-B offers three modes: tabular, surrogate, and raw. The tabular mode utilizes pre-calculated lookup tables for quick evaluations, while surrogate mode employs random forest models to approximate performance outcomes for arbitrary configurations. A system model is proposed to simulate realistic time consumption during raw mode evaluations, addressing the challenges posed by the disconnected simulation environments.
4. Extensibility and Open Source Availability: FedHPO-B is built upon the FederatedScope platform, ensuring ease of integration and extension. It provides tools for incorporating new FL tasks and HPO methods, supporting ongoing research and development. The benchmark suite is open-sourced, which encourages community involvement and continuous evolution.

Implications and Future Directions

The introduction of FedHPO-B represents a significant step toward standardized evaluation in federated HPO research. By explicitly addressing the unique characteristics of FL, this benchmark facilitates more accurate and representative assessments of HPO methods. Researchers can leverage this suite not only for verifying existing methods but also for developing novel strategies that optimize both hyperparameters and system resources.

Looking forward, the authors indicate plans to include support for emerging FL paradigms such as federated reinforcement learning and personalized federated learning. Another area of potential development is incorporating metrics for privacy preservation in HPO processes, an essential consideration given the sensitive nature of distributed data involved in FL.

Overall, FedHPO-B sets the groundwork for a more rigorous and expansive exploration of hyperparameter optimization strategies within federated learning environments. This benchmark suite therefore positions itself as a fundamental tool in advancing research and practice in AI, particularly in domains where privacy and data distribution are paramount.