Summary of Fedcal: Achieving Local and Global Calibration in Federated Learning Via Aggregated Parameterized Scaler, by Hongyi Peng et al.
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
by Hongyi Peng, Han Yu, Xiaoli Tang, Xiaoxiao Li
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores federated learning (FL), a technique that enables collaborative machine learning across distributed data owners. However, data heterogeneity poses a challenge for model calibration, which is critical to ensure accurate predictions. The authors reveal that existing FL aggregation approaches lead to sub-optimal calibration and provide theoretical analysis demonstrating the limitations of current methods. To address this issue, they propose Federated Calibration (FedCal), an innovative approach that emphasizes both local and global calibration. FedCal leverages client-specific scalers for local calibration, which are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Experimental results show that FedCal significantly outperforms existing methods, reducing global calibration error by 47.66% on average. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is like a team project where many people work together on a machine learning task without sharing their data. But sometimes, this can lead to problems because the different teams might have different ways of doing things. This paper looks at how we can make sure that everyone’s models are calibrated correctly, which means they’re making predictions in the same way. The authors show that existing methods don’t work very well and propose a new approach called Federated Calibration (FedCal). FedCal helps each team adjust their model so it works better with other teams. This leads to more accurate predictions overall. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
