AI RESEARCH
FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
arXiv CS.LG
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ArXi:2605.07962v1 Announce Type: new Performance evaluation is essential for assessing the quality of machine learning (ML) models and guiding deployment decisions. In federated learning (FL), assessing the performance is challenging because data are distributed across participants. Consequently, the coordinator must rely on locally computed evaluation metrics and aggregate them to assess the global model.