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Nanyang Technological University, Singapore
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FedTR achieves 95.5% accuracy in label defect identification, rivaling centralized models while preserving data privacy in federated learning settings.
Achieving over 100脳 reduction in computing power without sacrificing accuracy, this framework revolutionizes DNN inference on crossbar-based accelerators.
