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Federated learning with clustered hyperparameter optimization
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DOI：10.1016/j.ins.2025.122937.png)
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Abstract
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Hyperparameter tuning is crucial for improving machine learning model performance. However, the hyperparameter tuning process can be computationally expensive-typically, the entire hyper-parameter space is not searched, and some heuristic or statistically guided optimization algorithm is used. In federated learning, where there are many client models, hyperparameter tuning for each model becomes prohibitively expensive. In this work, we propose an efficient method to tune hyperparameters, by clustering clients based on their gradient updates and tuning hyperparameters individually for each cluster. We demonstrate that our proposed method leads to faster convergence compared to federated averaging, in both the i.i.d. and non-i.i.d. settings.
Keywords:
Federated learning
Bayesian optimization
Hyperparameter tuning
Clustering
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6.8
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