Multilingual Federated Low-Rank Adaptation for Collaborative Content Anomaly Detection across Multilingual Social Media Participants

ACL ARR 2025 May Submission4279 Authors

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.

Paper Type: Long

Research Area: Multilingualism and Cross-Lingual NLP

Research Area Keywords: Multilingual Federated Learning, Low-rank Adaption, Content Anomaly Detection on Social Media

Contribution Types: Approaches to low-resource settings

Languages Studied: Japanese, Chinese, Korean, French, English, Portuguese, Hindi, Spanish, Italian

Submission Number: 4279