F ed DQC : Data Quality Control in Federated Instruction-tuning of Large Language Models (2025)

Abstract: Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models (LLMs) by leveraging massively distributed data. However, the decentralized nature of FL exacerbates data quality challenges, as local clients lack global visibility to filter noisy or low-quality samples before training. To resolve this issue, we propose FedDQC, a novel federated instruction tuning framework with dynamic data quality control. Our approach introduces two key innovations. First, we propose instruction-response alignment (IRA)—an efficient client-side metric for quality evaluation requiring only low-cost inference. We validate that higher-IRA data corresponds to more relevant and easier-to-learn question-answer pairs. Second, mirroring the human easy-to-hard knowledge acquisition process, we design a quality-aware hierarchical FL training framework, where the LLM is progressively fine-tuned from high- to low-IRA data in a collaborative manner. The framework also supports adaptive data quality assessment at each hierarchy, enabling dynamic adjustments throughout the training process. Extensive experiments on synthetic and real-world datasets show that our method significantly improves LLM performance on mixed-quality data in FL.

## introduction for large language models (llms) training @xcite @xcite , both the quantity and quality of the training data significantly impact their performance @xcite . the scaling law suggests that more training data can lead to more powerful llms @xcite . however, in specific domains such as healthcare @xcite and q: 1000 men have provisions for 15 days. if 200 more men join them, for how many days will the provisions last now? a: 1000×15 = 1200x, @xmath0. if 200 more men join them, for how many days will the provisions last now? 1000×15 = 1200x, @xmath1.5 ## methodology this section presents the two-stage feddqc framework: the scoring stage and the training stage. firstly, section 4.1 gives an overview of feddqc. then section 4.2 and section 4.3 introduce the scoring and training stage respectively. ## disscussion communication, privacy and computation "privacy and communication efficiency are two primary concerns in fl". @xcite our proposed feddqc does not compromise on either of these aspects, as it does not introduce extra communication costs or privacy leakage, through training. regarding computation, feddqc adds only one step compared to fedavg: scoring all the training data, which only requires inferencing rather than training. when keeping the batch size the same for training and inference, the scoring time accounts for approximately 1% of the total training time. see section 6.4.2. we select the high-score data for later federated training. in our experiments, datainf and ifd are slightly adapted to federated scenarios, refer to appendix a.3 for more details. ## conclusions in this paper, we introduce feddqc, a novel framework for data quality control in federated instruction-tuning of llms. feddqc combines a new data quality assessment metric (ira) with federated hierarchical training, where data quality is dynamically evaluated during training. our experiments demonstrate that feddqc adds minimal computational overhead while significantly improving model performance. the integration of ira, adaptive scoring, and hierarchical training enhances both efficiency and robustness, making fed-dqc a promising approach for effective controlled data quality in mixed-quality distributed scenario.