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 $28.78\times$ 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.

Searchable encryption (SE) allows a data owner to outsource encrypted documents to an untrusted cloud server while preserving privacy and achieving secure data sharing. However, most existing SE schemes have a trade-off between security and efficiency. Moreover, these SE schemes leak the server's partial database or search information to perform better. Recent attacks show that such leakages can be used to recover the content of queried keywords or partial database information. To solve this problem and ensure efficiency, this paper proposes labeled searchable encryption (LSE), an efficient searchable encryption scheme based on the labeled private set intersection. We also give formal proofs to prove the security of the proposed labeled PSI protocol and searchable encryption scheme. Finally, we do experiments to compare the performance with some state-of-the-art works, and the experimental results show that the LSE outperforms in terms of total size and generation time of the encrypted database as well as the total search time at client side.