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By injecting basic physics, this method achieves up to 9% accuracy gains in human activity recognition, proving that inductive biases still matter for real-world sensor data.
Stop wasting bandwidth on irrelevant tokens: Fed-FSTQ uses Fisher information to selectively quantize and transmit only the most important tokens, slashing communication costs in federated LLM fine-tuning by up to 46x.
