In the context of real-world applications, leveraging large language models (LLMs) for domain-specific tasks often faces two major challenges: domain-specific knowledge privacy and constrained resources. To address these issues, we propose PDSS, a privacy-preserving framework for step-by-step distillation of LLMs. PDSS works on a server-client architecture, wherein client transmits perturbed prompts to the server's LLM for rationale generation. The generated rationales are then decoded by the client and used to enrich the training of task-specific small language model(SLM) within a multi-task learning paradigm. PDSS introduces two privacy protection strategies: the Exponential Mechanism Strategy and the Encoder-Decoder Strategy, balancing prompt privacy and rationale usability. Experiments demonstrate the effectiveness of PDSS in various text generation tasks, enabling the training of task-specific SLM with enhanced performance while prioritizing data privacy protection.

Large Language Models (LLMs), such as ChatGPT, LLaMA, GLM, and PaLM, have exhibited remarkable performances across various tasks in recent years. However, LLMs face two main challenges in real-world applications. One challenge is that training LLMs consumes vast computing resources, preventing LLMs from being adopted by small and medium-sized enterprises with limited computing resources. Another is that training LLM requires a large amount of high-quality data, which are often scattered among enterprises. To address these challenges, we propose FATE-LLM, an industrial-grade federated learning framework for large language models. FATE-LLM (1) facilitates federated learning for large language models (coined FedLLM); (2) promotes efficient training of FedLLM using parameter-efficient fine-tuning methods; (3) protects the intellectual property of LLMs; (4) preserves data privacy during training and inference through privacy-preserving mechanisms. We release the code of FATE-LLM at https://github.com/FederatedAI/FATE-LLM to facilitate the research of FedLLM and enable a broad range of industrial applications.

Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm in the industry. Its vertical federated learning version, SecureBoost, is one of the most popular algorithms used in cross-silo privacy-preserving modeling. As the area of privacy computation thrives in recent years, demands for large-scale and high-performance federated learning have grown dramatically in real-world applications. In this paper, to fulfill these requirements, we propose SecureBoost+ that is both novel and improved from the prior work SecureBoost. SecureBoost+ integrates several ciphertext calculation optimizations and engineering optimizations. The experimental results demonstrate that Secureboost+ has significant performance improvements on large and high dimensional data sets compared to SecureBoost. It makes effective and efficient large-scale vertical federated learning possible.