@inproceedings{li-etal-2025-multilingual-federated,
title = "Multilingual Federated Low-Rank Adaptation for Collaborative Content Anomaly Detection across Multilingual Social Media Participants",
author = "Li, Jiaxin and
Zhao, Geng and
Zhang, Xiaoci",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.770/",
doi = "10.18653/v1/2025.emnlp-main.770",
pages = "15242--15262",
ISBN = "979-8-89176-332-6",
abstract = "Recently, the rapid development of multilingual social media platforms (SNS) exacerbates new challenges in SNS content anomaly detection due to data islands and linguistic imbalance. While federated learning (FL) and parameter-efficient fine-tuning (PEFT) offer potential solutions in most cases, when every client is multilingual, existing solutions struggle with multilingual heterogeneity: 1) entangled language-specific knowledge during aggregation, 2) noise from minority languages, and 3) unstable cross-platform collaboration. Based on the asymmetric nature of LoRA, we propose MuLA-F, a multilingual Federated LoRA introducing SVD-based language-specific disentanglement of LoRA blocks and a local orthogonal tuning strategy. Evaluations across three SNS content anomaly detection tasks demonstrate MuLA-F{'}s superiority in multilingual performance while reducing multilingual knowledge conflicts and communication rounds."
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<abstract>Recently, the rapid development of multilingual social media platforms (SNS) exacerbates new challenges in SNS content anomaly detection due to data islands and linguistic imbalance. While federated learning (FL) and parameter-efficient fine-tuning (PEFT) offer potential solutions in most cases, when every client is multilingual, existing solutions struggle with multilingual heterogeneity: 1) entangled language-specific knowledge during aggregation, 2) noise from minority languages, and 3) unstable cross-platform collaboration. Based on the asymmetric nature of LoRA, we propose MuLA-F, a multilingual Federated LoRA introducing SVD-based language-specific disentanglement of LoRA blocks and a local orthogonal tuning strategy. Evaluations across three SNS content anomaly detection tasks demonstrate MuLA-F’s superiority in multilingual performance while reducing multilingual knowledge conflicts and communication rounds.</abstract>
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%0 Conference Proceedings
%T Multilingual Federated Low-Rank Adaptation for Collaborative Content Anomaly Detection across Multilingual Social Media Participants
%A Li, Jiaxin
%A Zhao, Geng
%A Zhang, Xiaoci
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F li-etal-2025-multilingual-federated
%X Recently, the rapid development of multilingual social media platforms (SNS) exacerbates new challenges in SNS content anomaly detection due to data islands and linguistic imbalance. While federated learning (FL) and parameter-efficient fine-tuning (PEFT) offer potential solutions in most cases, when every client is multilingual, existing solutions struggle with multilingual heterogeneity: 1) entangled language-specific knowledge during aggregation, 2) noise from minority languages, and 3) unstable cross-platform collaboration. Based on the asymmetric nature of LoRA, we propose MuLA-F, a multilingual Federated LoRA introducing SVD-based language-specific disentanglement of LoRA blocks and a local orthogonal tuning strategy. Evaluations across three SNS content anomaly detection tasks demonstrate MuLA-F’s superiority in multilingual performance while reducing multilingual knowledge conflicts and communication rounds.
%R 10.18653/v1/2025.emnlp-main.770
%U https://aclanthology.org/2025.emnlp-main.770/
%U https://doi.org/10.18653/v1/2025.emnlp-main.770
%P 15242-15262
Markdown (Informal)
[Multilingual Federated Low-Rank Adaptation for Collaborative Content Anomaly Detection across Multilingual Social Media Participants](https://aclanthology.org/2025.emnlp-main.770/) (Li et al., EMNLP 2025)
ACL