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.
Prompt distillation for efficient llm-based recommendation,
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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.