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arXiv Machine Learning By Maryam Moradpour, Anne-Christin Hauschild

FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification

Read the original on arXiv Machine Learning →

FedDRAW introduces a new server‑side aggregation technique for federated learning called Federated Dual Reputation Annealing Weighting. It blends a data‑size prior with cosine similarity between client and global model parameters, using two annealing schedules to shift weight from size to similarity and to gradually relax selectivity. Experiments on 12 simulated client partitions of CheXpert and ChestMNIST datasets show FedDRAW outperforms seven federated baselines in AUC and geometric mean of sensitivity and specificity, with statistically significant results.

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