FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
<|reference_start|>FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler: Federated learning (FL) enables collaborative machine learning across distributed data owners, but data heterogeneity poses a challenge for model calibration. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretical analysis shows despite constraining variance in clients' label distributions, global calibration error is still asymptotically lower bounded. To address this, we propose a novel Federated Calibration (FedCal) approach, emphasizing both local and global calibration. It leverages client-specific scalers for local calibration to effectively correct output misalignment without sacrificing prediction accuracy. These scalers are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Extensive experiments demonstrate FedCal significantly outperforms the best-performing baseline, reducing global calibration error by 47.66% on average.<|reference_end|>
@article{peng2024fedcal:,
    title={FedCal: Achieving Local and Global Calibration in Federated Learning via
  Aggregated Parameterized Scaler},
    author={Hongyi Peng, Han Yu, Xiaoli Tang, Xiaoxiao Li},
    journal={arXiv preprint arXiv:2405.15458},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15458},
    primaryClass={cs.LG cs.DC}
}