A framework generates synthetic neuroimages with explicit causal control via volumetric ROI changes to produce ground-truth data for benchmarking causal AI in neuroimaging.
Fesvibs: Federated split learning of vision transformer with block sampling
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SFLAM is a quantized split federated fine-tuning framework for large AI models that reduces device memory, energy use, and latency via split learning, optimization strategies, and simulations showing gains over conventional methods.
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A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI
A framework generates synthetic neuroimages with explicit causal control via volumetric ROI changes to produce ground-truth data for benchmarking causal AI in neuroimaging.
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Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning
SFLAM is a quantized split federated fine-tuning framework for large AI models that reduces device memory, energy use, and latency via split learning, optimization strategies, and simulations showing gains over conventional methods.