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    <abstract>Locate-then-Edit Knowledge Editing (LEKE) is a key technique for updating large language models (LLMs) without full retraining. However, existing methods assume a single-user setting and become inefficient in real-world multi-client scenarios, where decentralized organizations (e.g., hospitals, financial institutions) independently update overlapping knowledge, leading to redundant mediator knowledge vector (MKV) computations and privacy concerns.To address these challenges, we introduce Federated Locate-then-Edit Knowledge Editing (FedLEKE), a novel task that enables multiple clients to collaboratively perform LEKE while preserving privacy and reducing computational overhead. To achieve this, we propose FedEdit, a two-stage framework that optimizes MKV selection and reuse.In the first stage, clients locally apply LEKE and upload the computed MKVs. In the second stage, rather than relying solely on server-based MKV sharing, FedLEKE allows clients retrieve relevant MKVs based on cosine similarity, enabling knowledge re-edit and minimizing redundant computations.Experimental results on two benchmark datasets demonstrate that FedEdit retains over 96% of the performance of non-federated LEKE while significantly outperforming a FedAvg-based baseline by approximately twofold. Besides, we find that MEMIT performs more consistently than PMET in the FedLEKE task with our FedEdit framework. Our code is available at https://github.com/zongkaiz/FedLEKE.</abstract>
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%0 Conference Proceedings
%T FedLEKE: Federated Locate-then-Edit Knowledge Editing for Multi-Client Collaboration
%A Zhao, Zongkai
%A Xu, Guozeng
%A Li, Xiuhua
%A Wei, Kaiwen
%A Zhong, Jiang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhao-etal-2025-fedleke
%X Locate-then-Edit Knowledge Editing (LEKE) is a key technique for updating large language models (LLMs) without full retraining. However, existing methods assume a single-user setting and become inefficient in real-world multi-client scenarios, where decentralized organizations (e.g., hospitals, financial institutions) independently update overlapping knowledge, leading to redundant mediator knowledge vector (MKV) computations and privacy concerns.To address these challenges, we introduce Federated Locate-then-Edit Knowledge Editing (FedLEKE), a novel task that enables multiple clients to collaboratively perform LEKE while preserving privacy and reducing computational overhead. To achieve this, we propose FedEdit, a two-stage framework that optimizes MKV selection and reuse.In the first stage, clients locally apply LEKE and upload the computed MKVs. In the second stage, rather than relying solely on server-based MKV sharing, FedLEKE allows clients retrieve relevant MKVs based on cosine similarity, enabling knowledge re-edit and minimizing redundant computations.Experimental results on two benchmark datasets demonstrate that FedEdit retains over 96% of the performance of non-federated LEKE while significantly outperforming a FedAvg-based baseline by approximately twofold. Besides, we find that MEMIT performs more consistently than PMET in the FedLEKE task with our FedEdit framework. Our code is available at https://github.com/zongkaiz/FedLEKE.
%R 10.18653/v1/2025.findings-acl.733
%U https://aclanthology.org/2025.findings-acl.733/
%U https://doi.org/10.18653/v1/2025.findings-acl.733
%P 14247-14258

Markdown (Informal)

[FedLEKE: Federated Locate-then-Edit Knowledge Editing for Multi-Client Collaboration](https://aclanthology.org/2025.findings-acl.733/) (Zhao et al., Findings 2025)

• FedLEKE: Federated Locate-then-Edit Knowledge Editing for Multi-Client Collaboration (Zhao et al., Findings 2025)

ACL

• Zongkai Zhao, Guozeng Xu, Xiuhua Li, Kaiwen Wei, and Jiang Zhong. 2025. FedLEKE: Federated Locate-then-Edit Knowledge Editing for Multi-Client Collaboration. In Findings of the Association for Computational Linguistics: ACL 2025, pages 14247–14258, Vienna, Austria. Association for Computational Linguistics.