FED introduces a highly optimized GPU-accelerated framework that significantly improves dataset deduplication performance for training large language models.

• Implements non-cryptographic hash functions and GPU cluster optimization to achieve superior speed and efficiency
• Outperforms existing GPU-based deduplication methods through innovative technical optimizations
• Enhances data quality for LLM training with minimal computational overhead
• Demonstrates practical engineering solutions to a critical AI infrastructure challenge

This research addresses a key bottleneck in AI model development by providing a more efficient way to deduplicate massive datasets, ultimately improving both training efficiency and model performance.

FED: Fast and Efficient Dataset Deduplication Framework with GPU Acceleration