Anke Schmeink

Optimal data selection for LLMs is not one-size-fits-all; it varies dramatically based on task objectives and resource constraints.

Compressing federated learning models with pruning, quantization, and Huffman coding yields an 11x reduction in model size and 60% faster training with only a 2% accuracy drop.