Can Machine Learning Evolve Beyond Public Data Limits? This Research from China Introduces OpenFedLLM: Pioneering Collaborative and Privacy-Preserving Training of Large Language Models Using Federated Learning

LLMs, trained on extensive public datasets, have shown remarkable success across various fields, but the depletion of high-quality public data is imminent by 2026. Due to this scarcity, researchers combine existing datasets or generate model-created data. However, abundant high-quality data must still be utilized due to privacy or logistical constraints. For instance, BloomberGPT excels in […]

The post Can Machine Learning Evolve Beyond Public Data Limits? This Research from China Introduces OpenFedLLM: Pioneering Collaborative and Privacy-Preserving Training of Large Language …

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