Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research

arXiv cs.AI · 6 hours ago

Researchers developed an adaptive layer-freezing strategy for federated learning that reduces energy consumption in medical imaging tasks by selectively freezing encoder weights during training. The approach achieved up to 23% reductions in training time, energy consumption, and CO2 emissions while maintaining performance on MRI-to-CT conversion across multiple architectures. The method enables healthcare institutions with limited computational resources to participate in collaborative model training without significant performance trade-offs.