Efficient and Privacy-Preserving Training and Inference in Large Language Models

The recent developments in the field of large language models (LLMs) and federated learning (FL) have shown a significant shift towards more efficient and privacy-preserving training and inference methods. Researchers are increasingly focusing on optimizing the computational and communication costs associated with training LLMs, particularly in distributed environments. Novel approaches such as differentially private synthetic sample generation and cross-cloud federated training are being explored to address these challenges, enabling more scalable and secure model training. Additionally, there is a growing interest in model compression techniques that allow for the deployment of LLMs in resource-constrained environments without significant loss in performance. These advancements are paving the way for more accessible and efficient AI systems, particularly in scenarios where data privacy and computational efficiency are paramount. Notably, the introduction of training-free compensation methods for compressed LLMs and the optimization of sample compute allocation during inference are particularly innovative, offering scalable solutions that enhance both performance and efficiency.

Noteworthy Papers:

  • LanFL: Introduces a novel prompt-based FL scheme for LLMs, leveraging differentially private synthetic samples for efficient knowledge sharing.
  • EoRA: Proposes a training-free compensation method for compressed LLMs, significantly improving performance across various tasks.

Sources

LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples

Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models

Notes on the Mathematical Structure of GPT LLM Architectures

Computational Bottlenecks of Training Small-scale Large Language Models

A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing

EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation

Mathematical Derivation Graphs: A Task for Summarizing Equation Dependencies in STEM Manuscripts

How Does Critical Batch Size Scale in Pre-training?

Scaling LLM Inference with Optimized Sample Compute Allocation

Does equivariance matter at scale?

$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources

Built with on top of