FedRW: Efficient Privacy-Preserving Data Reweighting for Enhancing Federated Learning of Language Models
This addresses data duplication and privacy vulnerabilities in federated learning for language models, establishing a new paradigm rather than being incremental.
The paper tackles the problem of data duplication impairing performance and privacy in federated learning of language models by proposing FedRW, a privacy-preserving framework that uses sample reweighting instead of deletion, resulting in up to 28.78x speedup in preprocessing and approximately 11.42% improvement in perplexity.
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.