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Late fusion in quantum machine learning achieves near-identical accuracy to full reconstruction while slashing costs and enhancing noise resilience.
DMTT is the only decentralized federated learning method that maintains high accuracy against adversarial attacks while ensuring Byzantine influence is effectively minimized.
FedCARE achieves up to 12.5% better predictive accuracy by enabling personalised model adaptations in federated healthcare settings without compromising data privacy.
Achieving up to 40.3% cost savings in LLM inference through optimal prefix key-value placement could redefine resource allocation strategies in AI deployments.
PrefixShield transforms how multi-tenant LLMs manage shared resources, achieving up to an 84.87% victim cache hit ratio by addressing the admission-responsibility gap.
