用加权代替删除，安全提升联邦语言模型训练效率与隐私保护
FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
- 通过安全多方计算实现无信任第三方的样本加权去重
- 预处理速度提升28.78倍，困惑度降低11.42%
- 适合关注隐私保护与高效训练的联邦学习研究者
大规模语料中的数据重复会损害大语言模型性能并带来隐私风险。在隐私敏感的联邦学习场景中，传统去重方法通常依赖可信第三方进行统一删除，既可能丢失有价值样本，又引入隐私泄露风险。为此，我们提出联邦重加权（FedRW），据我们所知是首个无需可信第三方的隐私保护框架，通过样本重加权而非删除实现软去重。核心是基于安全多方计算的频率感知重加权协议，并结合并行编排策略保证高效可扩展。训练中，利用全局样本频率自适应调整个体损失贡献，有效提升泛化性与鲁棒性。实验表明，相比最先进方法，FedRW在预处理上提速28.78倍，困惑度改善约11.42%，同时提供更强安全保障。该工作确立了联邦大模型训练中处理重复数据的新范式。
原文摘要 · Abstract (English)
Data duplication within large-scale corpora often impedes large language models' (LLMs) performance and privacy. In privacy-concerned federated learning scenarios, conventional deduplication methods typically rely on trusted third parties to perform uniform deletion, risking loss of informative samples while introducing privacy vulnerabilities. To address these gaps, we propose Federated ReWeighting (FedRW), the first privacy-preserving framework, to the best of our knowledge, that performs soft deduplication via sample reweighting instead of deletion in federated LLM training, without assuming a trusted third party. At its core, FedRW proposes a secure, frequency-aware reweighting protocol through secure multi-party computation, coupled with a parallel orchestration strategy to ensure efficiency and scalability. During training, FedRW utilizes an adaptive reweighting mechanism with global sample frequencies to adjust individual loss contributions, effectively improving generalization and robustness. Empirical results demonstrate that FedRW outperforms the state-of-the-art method by achieving up to 28.78x speedup in preprocessing and approximately 11.42% improvement in perplexity, while offering enhanced security guarantees. FedRW thus establishes a new paradigm for managing duplication in federated LLM training.
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