Concordia：面向联邦大语言模型的自改进合成表格
Concordia: Self-Improving Synthetic Tables for Federated LLMs
AI总结：
本文研究了在无法共享原始数据的情况下，如何通过自改进的合成表格来提升联邦学习中大语言模型的适应能力，提出了一种三层优化框架Concordia，通过参数高效LoRA训练和轻量级效用评分器提升联邦验证效用和跨客户端稳定性。
摘要：
联邦学习（FL）能够在不共享原始数据的情况下训练大型语言模型（LLMs），但在严格的数据隔离和非独立同分布（non-IID）客户端分布下，适应LLMs仍然具有挑战性。合成数据为本地训练提供了自然的隐私保护替代方案，但现有联邦流程通常将合成生成视为静态或松散耦合于下游优化，导致在异质客户端下效用迅速下降。我们研究了在无法共享原始记录和验证数据的情况下，如何在表格任务中进行联邦适应，并且本地训练必须完全依赖合成表格。我们提出Concordia，一种三层优化框架，该框架在这些约束下对齐合成数据生成与联邦验证效用。在客户端层面，模型通过参数高效LoRA训练在合成表格上进行适应。客户端还从私有验证反馈中学习轻量级效用评分器，以在本地训练中重新加权合成样本。在外层，每个客户端使用组相对策略优化（GRPO）来细化自己的合成表格生成器，由跨客户端共享的异质评分器集合引导，而无需聚合生成器参数或暴露验证数据。在隐私敏感的表格基准测试中，Concordia在金融和医疗领域展示了比静态和解耦合成数据基线更一致的联邦性能、跨客户端稳定性和对分布偏移的鲁棒性。
英文摘要：
Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-IID client distributions remains challenging in practice. Synthetic data offers a natural privacy-preserving surrogate for local training, yet existing federated pipelines typically treat synthetic generation as static or loosely coupled with downstream optimization, leading to rapidly diminishing utility under heterogeneous clients. We study federated adaptation of LLMs on tabular tasks where raw records and validation data cannot be shared, and local training must rely entirely on synthetic tables. We propose Concordia, a tri-level optimization framework that aligns synthetic data generation with federated validation utility despite these constraints. At the client level, models are adapted via parameter-efficient LoRA training on synthetic tables. Clients additionally learn lightweight utility scorers from private validation feedback to reweight synthetic samples during local training. At the outer level, each client refines its own synthetic table generator using group-relative policy optimization (GRPO), guided by an ensemble of heterogeneous scorers shared across clients, without aggregating generator parameters or exposing validation data. Experiments on privacy-sensitive tabular benchmarks from finance and healthcare demonstrate that Concordia consistently improves federated performance, cross-client stability, and robustness to distribution shift compared to static and decoupled synthetic-data baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
