Federated Hyperparameter Optimization Through Reward-Based Strategies: Challenges and Insights

Krishna Kanth Nakka, Ahmed Frikha, Ricardo Mendis, Xue Jiang, Xuebing Zhou; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4236-4244

Performing hyperparameter tuning in federated learning is often prohibitively expensive due to the substantial communication overhead associated with training a single configuration especially with a large hyperparameter search space. To overcome this challenge recent works explored reward-based approaches to learn a policy distribution over a set of hyperparameter configurations. These approaches enable the concurrent exploration of multiple hyperparameter configurations within a single communication round thereby accelerating the search process. In this paper we take a deeper look at the reward-based strategies and systematically analyze them uncovering several issues and challenges associated with their adoption in practice.Furthermore motivated by the insights from our analysis we propose an in-depth evaluation of policy distribution with metrics that capture rankings of standalone configurations. We contribute this critical examination and proposed evaluation metrics in order to raise awareness about the challenges and hidden issues that reward-based federated hyperparameter optimization might face and to enable a more rigorous evaluation and therefore a faster progress in this research area. We expect that the identified challenges will serve as inspiration for the development of more robust and hyperparameter-free federated hyperparameter tuning approaches.