用概率化提示聚合解决联邦学习中的异构数据问题
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
- 将联邦学习转为分布式提示集建模，通过优化输入前缀重编程模型
- 在多个视觉数据集上显著优于传统方法，有效应对极端数据异构性
- 适合处理非独立同分布且不平衡的联邦学习场景
微调预训练模型是解决复杂任务的常用方法，但在联邦学习中，本地数据分布高度偏斜时全模型微调效果不佳。为此，本文探索将联邦学习与更高效的提示微调结合，仅优化少量输入前缀以改变预训练模型行为。该方法将联邦学习转化为分布式提示集建模任务，通过聚合多样化的提示集实现全局模型优化。我们在多种计算机视觉数据集上对比了现有联邦聚合技术的直接适配方法，并提出一种新的概率提示聚合方法，结果表明该方法显著优于基线。实验验证了所提方法在极端数据异构条件下最有效。
原文摘要 · Abstract (English)
Fine-tuning pre-trained models is a popular approach in machine learning for solving complex tasks with moderate data. However, fine-tuning the entire pre-trained model is ineffective in federated data scenarios where local data distributions are diversely skewed. To address this, we explore integrating federated learning with a more effective prompt-tuning method, optimizing for a small set of input prefixes to reprogram the pre-trained model's behavior. Our approach transforms federated learning into a distributed set modeling task, aggregating diverse sets of prompts to globally fine-tune the pre-trained model. We benchmark various baselines based on direct adaptations of existing federated model aggregation techniques and introduce a new probabilistic prompt aggregation method that substantially outperforms these baselines. Our reported results on a variety of computer vision datasets confirm that the proposed method is most effective to combat extreme data heterogeneity in federated learning.
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