FL

Coordinated Replay Sample Selection for Continual Federated Learning (2023.emnlp-industry)

Challenge:	Increasing concerns and regulations about data privacy necessitate the study of privacy-preserving, decentralized learning methods for natural language processing tasks.
Approach:	They propose a framework for evaluating federated learning methods on four different tasks . they propose federation between Transformer-based language models and FL methods .
Outcome:	The proposed framework compares FL methods on four different tasks under non-IID partitioning strategies.

Copied to clipboard

Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard Zemel, Rahul Gupta

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)

Challenge:	Continual Federated Learning (CFL) combines decentralized learning with continuous learning . ubiquity of personal devices with a network connection offers rich source of data for learning problems .
Approach:	They propose to combine decentralized learning with a continuous learning approach . they propose to coordinate gradient-based replay sample selection across clients .
Outcome:	The proposed method shows gains early in the low replay size regime, when the budget for storing past data is small.

Copied to clipboard

Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun

Empirical Studies of Institutional Federated Learning For Natural Language Processing (2020.findings-emnlp)

Challenge:	Existing frameworks that share entity embeddings of knowledge graphs (KGs) would incur a severe privacy leakage.
Approach:	They propose a new attack method that aims to recover the original embedding information based on the known entity embeddables of FedE.
Outcome:	The proposed framework can be used to infer whether a specific relation exists in a private client.

Copied to clipboard

Xinghua Zhu, Jianzong Wang, Zhenhou Hong, Jing Xiao

Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning (2026.acl-short)

Challenge:	federated learning is a promising ideology to unite isolated datasets for machine learning problems.
Approach:	They propose to use federated natural language processing networks to train a popular NLP model with applications in sentence intent classification.
Outcome:	The proposed model is sensitive to imbalanced data load and tested against a federated model under imbalanced datasets.

Copied to clipboard

Tinnakit Udsa, Can Udomcharoenchaikit, Patomporn Payoungkhamdee, Sarana Nutanong, Norrathep Rattanavipanon

FL-MSCL: A Unified Figurative Language Detection Model Driven by Multi-Type Signals and Contrastive Learning (2026.acl-short)

Challenge:	Existing methods to assess memorization in federated learning focus on one sample at a time . centralized learning does not eliminate the risk of memorizing large language models .
Approach:	They propose a framework that quantifies both intra- and inter-client memorization in FL . they use fine-grained cross-sample memorisation measurement across all clients .
Outcome:	The proposed framework quantifies both intra- and inter-client memorization in FL using fine-grained cross-sample memorisation measurement across all clients.

Copied to clipboard

Lu Shijia, Fumiyo Fukumoto, Huang Xiaoxi, Yoshimi Suzuki

Can Public Large Language Models Help Private Cross-device Federated Learning? (2024.findings-naacl)

Challenge:	Figurative language recognition challenges distinguishing between fine-grained rhetorical categories . existing approaches are framed as single-category binary classifiers .
Approach:	They propose a framework that integrates prompt-based knowledge injection with supervised contrastive learning to enforce explicit class distinctions.
Outcome:	The proposed framework achieves competitive performance on a four-way sentence-level classification task.

Copied to clipboard

Boxin Wang, Yibo Zhang, Yuan Cao, Bo Li, Hugh McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer

Federated Learning of Gboard Language Models with Differential Privacy (2023.acl-industry)

Challenge:	Recent studies have shown that public data can be used to improve privacy-utility trade-offs for large and small language models.
Approach:	They propose to use large-scale public data to help differentially private FL training . they propose a distribution matching algorithm with theoretical grounding to sample public data close to private data distribution .
Outcome:	The proposed method is efficient and effective for training private models by taking advantage of public data.

Copied to clipboard

Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher Choquette, Peter Kairouz, Brendan Mcmahan, Jesse Rosenstock, Yuanbo Zhang

A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems (2024.emnlp-industry)

Challenge:	Using federated learning and differential privacy, we train and deploy language models with federation and DP in Google Keyboard.
Approach:	They train and deploy language models with federated learning and differential privacy in Google Keyboard .
Outcome:	The proposed algorithm achieves meaningfully formal DP guarantees without uniform sampling of clients.

Copied to clipboard

Hugh McMahan, Zheng Xu, Yanxiang Zhang

Client-Customized Adaptation for Parameter-Efficient Federated Learning (2023.findings-acl)

Challenge:	Differential privacy (DP) and federated learning (FL) are used for language models training in production mobile keyboard applications.
Approach:	They propose a variant of DP-FTRL that uses a correlated noise mechanism to train on-device language models.
Outcome:	The proposed method improves privacy-utility trade-off and memory efficiency over existing FL methods while simplifying usage requirements and reducing memory.

Copied to clipboard

Yeachan Kim, Junho Kim, Wing-Lam Mok, Jun-Hyung Park, SangKeun Lee

Green & Sustainable NLP

Challenge:	Pre-trained language models have a large memory footprint and are difficult to use in federated learning (FL)
Approach:	They propose a hypernetwork-based FL framework that generates client-specific adapters by conditioning the client information.
Outcome:	The proposed framework maximizes the utility of shared model parameters while minimizing divergence caused by client heterogeneity.

Pretrained Models for Multilingual Federated Learning (2022.naacl-main)

Copied to clipboard

Orion Weller, Marc Marone, Vladimir Braverman, Dawn Lawrie, Benjamin Van Durme

Training Mixed-Domain Translation Models via Federated Learning (2022.naacl-main)

Challenge:	Federated Learning (FL) is a machine learning technique that trains a model across multiple distributed clients holding local data samples, without ever storing client data in a central location.
Approach:	They propose to use pretrained models to study three multilingual language tasks . they also examine impact of non-IID text on FL in naturally occurring data .
Outcome:	The proposed methods perform better than centralized learning even when using non-IID partitioning.

Copied to clipboard

Peyman Passban, Tanya Roosta, Rahul Gupta, Ankit Chadha, Clement Chung

FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP (2023.acl-long)

Challenge:	Experimental results show that neural machine translation engines built via FL can be easily adapted when an FL-based aggregation is applied to fuse different domains.
Approach:	They propose to use federated learning to fuse mixed-domain translation models with a centralized aggregation to improve their performance.
Outcome:	The proposed model can be easily adapted to a mixed-domain translation model with slight modifications in the training process and perform on par with state-of-the-art training models.

Copied to clipboard

Zhuo Zhang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Lizhen Qu, Zenglin Xu

Federated Learning with Noisy User Feedback (2022.naacl-main)

Challenge:	federated learning (FL) is a promising technique for preserving data privacy . however, there is no work on applying FL to legal NLP .
Approach:	They propose to use federated learning to train models in a collaborative way without sharing data . they propose to test the FL benchmark on real-world legal data from Chinese courts .
Outcome:	The proposed benchmark combines five legal NLP tasks and one privacy task on Chinese courts.

Copied to clipboard

Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta

FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation (2026.findings-eacl)

Challenge:	Artificial Intelligence (AI) and Machine Learning (ML) systems are becoming more popular and are causing concerns over user privacy.
Approach:	They propose a method for training ML models using positive and negative user feedback and a framework to extract labels on edge to make FL viable.
Outcome:	The proposed method improves significantly over a self-training baseline, achieving performance closer to models trained with full supervision.

Copied to clipboard

Fatema Siddika, Md Anwar Hossen, Juan Pablo Munoz, Tanya G. Roosta, Anuj Sharma, Ali Jannesari

pFedGPT: Hierarchically Optimizing LoRA Aggregation Weights for Personalized Federated GPT Models (2025.emnlp-main)

Challenge:	Representation Fine-Tuning (ReFT) adapts large pre-trained models by updating only a small subset of parameters.
Approach:	They propose a method that uses sparse intervention layers to steer hidden representations directly to capture rich semantic information.
Outcome:	The proposed approach outperforms PEFTs on commonsense reasoning, arithmetic reasoning, and GLUE benchmarks while maintaining a high parameter efficiency.

Copied to clipboard

Zhanming Shen, Tianqi Xu, Hao Wang, Jian Li, Miao Pan

Safely Learning with Private Data: A Federated Learning Framework for Large Language Model (2024.emnlp-main)

Challenge:	Existing methods for fine-tuning Large Language Models (LLMs) struggle with data heterogeneity and adapt shared global knowledge to individual client needs.
Approach:	They propose a framework that leverages Hierarchical Bayesian Optimization (HBO) for fine-grained, personalized LoRA aggregation.
Outcome:	The proposed framework achieves state-of-the-art (SOTA) performance on personalized FL benchmarks while introducing only minimal (approx. 4%) additional optimization overhead.

Copied to clipboard

Jia-Ying Zheng, Hainan Zhang, Lingxiang Wang, Wangjie Qiu, Hong-Wei Zheng, Zhi-Ming Zheng

Open-Vocabulary Federated Learning with Multimodal Prototyping (2024.naacl-long)

Challenge:	Existing large language models (LLMs) use large amounts of public data and massive parameters, but private data is often stored in isolated data silos.
Approach:	They propose a Federated Learning framework for large language models which offloads most training parameters to the server while training embedding and output layers locally.
Outcome:	The proposed framework achieves comparable metrics to centralized chatGLM model on NLU and generation tasks.

Copied to clipboard

Huimin Zeng, Zhenrui Yue, Dong Wang

Multimodality

Challenge:	Existing studies assume the label space of training data and test data is identical.
Approach:	They propose a framework for adaptation to a federated learning (FL) query that uses arbitrary unknown classes.
Outcome:	The proposed framework exploits the knowledge learned from seen classes and robustifies the adapted framework to unseen categories.

Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories (2021.emnlp-main)

Copied to clipboard

Han Qin, Guimin Chen, Yuanhe Tian, Yan Song

Sentiment Analysis

Challenge:	Aspect-based sentiment analysis (ABSA) predicts sentiment polarity for aspect term in sentences . labeled data stored at different locations and inaccessible due to privacy or legal concerns .
Approach:	They propose a model with federated learning to combine labeled data across different domains . they incorporate topic memory to take data from diverse domains into consideration .
Outcome:	The proposed model outperforms baselines on a simulated environment with three nodes.

Federated Continual Learning for Text Classification via Selective Inter-client Transfer (2022.findings-emnlp)

Copied to clipboard

Yatin Chaudhary, Pranav Rai, Matthias Schubert, Hinrich Schütze, Pankaj Gupta

Information Extraction & Text Mining

Challenge:	Continual Learning (CL) is a privacy-preserving machine learning technique that enables collaborative training of ML models by sharing model parameters across distributed clients.
Approach:	They propose a framework which selectively combines model parameters of foreign clients to maximize knowledge transfer while preserving privacy.
Outcome:	The proposed framework improves the performance of a text classification task using five datasets from diverse domains while preserving privacy.

Federated Chinese Word Segmentation with Global Character Associations (2021.findings-acl)

Copied to clipboard

Yuanhe Tian, Guimin Chen, Han Qin, Yan Song

Syntactic Text Processing

Challenge:	Chinese word segmentation (CWS) is a fundamental task for natural language processing.
Approach:	They propose a neural model for Chinese word segmentation with federated learning to help CWS deal with data isolation.
Outcome:	The proposed model outperforms baselines on a simulated environment with five nodes.

X-FLoRA: Cross-modal Federated Learning with Modality-expert LoRA for Medical VQA (2025.emnlp-main)

Copied to clipboard

Min Hyuk Kim, Changheon Kim, Seok Bong Yoo

Federated Model Decomposition with Private Vocabulary for Text Classification (2022.emnlp-main)

Challenge:	Medical visual question answering (VQA) and federated learning (FL) are important tools for privacy-preserving collaborative learning.
Approach:	They propose a cross-modal FL framework that uses modality-expert low-rank adaptation for medical visual question answering (VQA) X-FLoRA enables the synthesis of images from one modality to another without requiring data sharing .
Outcome:	Experiments show that X-FLoRA outperforms existing FL methods in terms of performance . XFLorage enables synthesis of images from one modality to another without data sharing .

Copied to clipboard

Zhuo Zhang, Xiangjing Hu, Lizhen Qu, Qifan Wang, Zenglin Xu

Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization (2023.emnlp-main)

Challenge:	Existing methods to train federated learning (FL) for natural language processing require sensitive data to leave local devices.
Approach:	They propose a fedrated model decomposition method that protects the privacy of vocabularies . they propose an adaptive updating technique to improve the performance of local models .
Outcome:	The proposed method protects the privacy of vocabularies in federated learning tasks . it maintains competitive performance and provides better privacy-preserving capacity compared to status quo methods.

Copied to clipboard

Tianshi Che, Ji Liu, Yang Zhou, Jiaxiang Ren, Jiwen Zhou, Victor Sheng, Huaiyu Dai, Dejing Dou

FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models (2023.emnlp-main)

Challenge:	Prompt tuning of Large Language Models (LLMs) can incur performance degradation or low training efficiency.
Approach:	They propose a prompt tuning approach with Adaptive Optimization to enable efficient FL of LLMs.
Outcome:	The proposed approach improves performance and efficiency simultaneously and addresses client drift problems on both the device and server sides.

Copied to clipboard

Xinge Ma, Jiangming Liu, Jin Wang, Xuejie Zhang

Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models (2024.emnlp-main)

Challenge:	federated learning (FL) is widely studied in user-related natural language processing (NLP) but its performance is faded by confirmation bias.
Approach:	They propose a decentralized learning paradigm that uses labeled data to rectify local models . they propose federated interactive distillation (FedID) to alleviate communication overhead .
Outcome:	The proposed framework achieves the best results in homogeneous and heterogeneously federated scenarios.

Copied to clipboard

Ji Liu, Jiaxiang Ren, Ruoming Jin, Zijie Zhang, Yang Zhou, Patrick Valduriez, Dejing Dou

Fair Federated Learning with Biased Vision-Language Models (2024.findings-acl)

Challenge:	Existing frameworks for learning Large Language Models (LLMs) require adaptive data processing and low-rank adjustment to improve accuracy and fine-tuning speed.
Approach:	They propose a fisher information-based adaptive federated curriculum learning framework with two novel methods to improve FL fine-tuning process.
Outcome:	The proposed framework improves performance and fine-tuning speed compared with baseline approaches.

Copied to clipboard

Huimin Zeng, Zhenrui Yue, Yang Zhang, Lanyu Shang, Dong Wang

A Secure and Efficient Federated Learning Framework for NLP (2021.emnlp-main)

Challenge:	Existing literature ignores the inherent group unfairness within CLIP and its ethical implications on FL applications.
Approach:	They propose a fairness-aware adaptation framework for CLIP in federated learning . they propose to leverage biased pre-trained VLMs to build fair FL frameworks .
Outcome:	The proposed framework addresses unique bias in FL, triggered by data heterogeneity . it trains a fair FL model with fairness-aware deep visual prompting (DVP) Extensive results on human face attribute recognition (FAR) applications show it outperforms state-of-the-art FL models .

Copied to clipboard

Chenghong Wang, Jieren Deng, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding

Green & Sustainable NLP

Challenge:	Existing FL frameworks require a trusted aggregator or require heavy-weight cryptographic primitives, which makes the performance significantly degraded.
Approach:	They propose a framework that is federated and efficient for NLP . they propose to eliminate the need for trusted entities and achieve better model accuracy .
Outcome:	The proposed framework achieves better model accuracy and model accuracy than existing FL frameworks.

Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models (2024.lrec-main)

Copied to clipboard

Sixing Yu, Juan Pablo Munoz, Ali Jannesari

FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023.findings-acl)

Challenge:	Foundation Models (FMs) have demonstrated success in a wide range of applications, but their optimization often requires access to sensitive data.
Approach:	They propose a framework that combines FMs and Federated Learning to enable privacy-preserving and collaborative learning across multiple end-users.
Outcome:	The proposed framework combines benefits of FMs and Federated Learning (FL) it enables privacy-preserving and collaborative learning across multiple end-users.

Copied to clipboard

Zhuo Zhang, Yuanhang Yang, Yong Dai, Qifan Wang, Yue Yu, Lizhen Qu, Zenglin Xu

TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts (2024.emnlp-main)

Challenge:	Existing research on federated learning (FL) for pre-trained language models (PLMs) with increasing concerns about data privacy, enterprises or institutions are not allowed to collect data from end devices or local clients to a centralized server for fine-tuning PLMs.
Approach:	They investigate the parameter-efficient tuning of pre-trained language models (PLMs) and develop a federated benchmark for four representative PETuning methods .
Outcome:	The proposed method can defend against privacy attacks and maintain acceptable performance with reducing heavy resource consumption.

Copied to clipboard

Ruida Wang, Jipeng Zhang, Yizhen Jia, Rui Pan, Shizhe Diao, Renjie Pi, Tong Zhang

Reasoning

Challenge:	a framework for formal proof writing using formal languages like Lean4 is needed to prove mathematical theorems using formal language.
Approach:	They propose a framework that trains a general-purpose LLM to be a Lean4 expert.
Outcome:	The proposed framework achieves cumulative accuracies of 36.48% and 33.61% on MiniF2F-Valid and Test datasets.

Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms (2023.acl-long)

Copied to clipboard

Tianshu Zhang, Changchang Liu, Wei-Han Lee, Yu Su, Huan Sun

pFedRAG: A Personalized Federated Retrieval-Augmented Generation System with Depth-Adaptive Tiered Embedding Tuning (2025.findings-emnlp)

Challenge:	Neural semantic parsers have achieved remarkable performance in recent years, but they are data-hungry and require annotators to have intimate knowledge of formal programs.
Approach:	They propose a task where multiple clients collaboratively train one global model without sharing their semantic parsing data.
Outcome:	The proposed model improves performance on three widely adopted FL algorithms (FedAvg, FedOPT and FedProx) and clients with smaller datasets enjoy faster performance.

Copied to clipboard

Hangyu He, Xin Yuan, Kai Wu, Ren Ping Liu, Wei Ni

Multilingual Federated Low-Rank Adaptation for Collaborative Content Anomaly Detection across Multilingual Social Media Participants (2025.emnlp-main)

Challenge:	Personalized Federated RAG framework enables efficient collaborative fine-tuning of embedding models . depth-adaptive tieered Embedding (DATE) architecture is tailored for local data and training results of each client.
Approach:	a new Personalized Federated RAG framework is proposed for large language models . the framework enables efficient collaborative fine-tuning of embedding models based on common knowledge .
Outcome:	a novel Personalized Federated RAG framework is proposed for large language models . the framework enables efficient collaborative fine-tuning of embedding models based on common knowledge .

Copied to clipboard

Jiaxin Li, Geng Zhao, Xiaoci Zhang

Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling (2024.findings-acl)

Challenge:	Recent developments in multilingual social media platforms (SNS) exacerbate new challenges in SNS content anomaly detection due to data islands and linguistic imbalance.
Approach:	They propose a multilingual Federated LoRA based on SVD-based language-specific disentanglement of LoRA blocks and a local orthogonal tuning strategy to detect content anomalies.
Outcome:	The proposed solution is superior in multilingual content anomaly detection while reducing multilingual knowledge conflicts and communication rounds.

Copied to clipboard

Gregorios Katsios, Ning Sa, Tomek Strzalkowski

FedDQC: Data Quality Control in Federated Instruction-tuning of Large Language Models (2025.findings-acl)

Challenge:	Existing models that detect multiple FL features in text are not effective in authorship attribution tasks.
Approach:	They propose a multi-task Figurative Language Model that learns to detect multiple FL features in text at once.
Outcome:	The proposed model outperforms specialized binary models in AA tasks or outperformed binary models on three datasets.

Copied to clipboard

Yaxin Du, Rui Ye, Fengting Yuchi, Wanru Zhao, Jingjing Qu, Yanfeng Wang, Siheng Chen

Federated Data-Efficient Instruction Tuning for Large Language Models (2025.findings-acl)

Challenge:	Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models.
Approach:	They propose a federated instruction tuning framework with dynamic data quality control to solve this problem.
Outcome:	The proposed framework improves performance on mixed-quality datasets on synthetic and real-world datasets.

Copied to clipboard

Zhen Qin, Zhaomin Wu, Bingsheng He, Shuiguang Deng

Let’s Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM’s Math Capability (2025.emnlp-main)

Challenge:	Existing federated learning (FL) uses all local data, causing excessive computational overhead and overfitting to local data.
Approach:	They propose a federated data-efficient instruction tuning approach which utilizes a representative subset of edge-side data to tune LLMs.
Outcome:	The proposed method improves Rouge-L on unseen tasks by 10.72% over the SOTA full-data instruction tuning methods while using less than 1.5% of the data samples.

Copied to clipboard

Ruida Wang, Yuxin Li, Yi R. Fung, Tong Zhang

Reasoning

Challenge:	Recent work has focused on improving the mathematical reasoning capabilities of Large Language Models (LLMs).
Approach:	They propose an end-to-end framework to integrate FL into NL math reasoning . they propose a problem alignment method that reformulates QA and existence problems .
Outcome:	The proposed framework achieves 89.80% and 84.34% accuracy rates on the MATH-500 and the AMC benchmarks.

Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults (2026.acl-long)

Copied to clipboard

Zhenhao Zhou, Zhuochen Huang, Yike He, Chong Wang, Jiajun Wang, Yijian Wu, Xin Peng, Yiling Lou

Challenge:	Existing LLM agents struggle with identifying bugs in the Linux kernel . bugs can affect billions of users, affecting the Linux Foundation's research on the topic .
Approach:	They propose a LinuxFLBench benchmark to measure the accuracy of LLM agents on the Linux kernel.
Outcome:	The proposed framework improves FL accuracy with minimal costs.

Tunable Soft Prompts are Messengers in Federated Learning (2023.findings-emnlp)

Copied to clipboard

Chenhe Dong, Yuexiang Xie, Bolin Ding, Ying Shen, Yaliang Li

EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models (2025.emnlp-main)

Challenge:	Existing methods to protect model privacy in federated learning (FL) are limited.
Approach:	They propose a federated learning approach that provides model privacy protection via tunable soft prompts.
Outcome:	The proposed approach provides protection for the global model while reducing communication and computation costs.

Copied to clipboard

Han Liu, Ruoyao Wen, Srijith Nair, Jia Liu, Wenjing Lou, Chongjie Zhang, William Yeoh, Yevgeniy Vorobeychik, Ning Zhang

Safe-FedLLM: Delving into the Safety of Federated Large Language Models (2026.acl-long)

Challenge:	Recurrent exchange of model updates in FL can result in prohibitively high communication costs, hindering the distributed learning process.
Approach:	They propose a federated fine-tuning framework that uses a round-robin segment sharing scheme to reduce network bandwidth and adaptive sparsification methods tailored to LoRA’s training dynamics.
Outcome:	The proposed framework reduces communication overhead without compromising performance on question-answering and value-alignment tasks.

Copied to clipboard

Mingxiang Tao, Yu Tian, Wenxuan Tu, Yue Yang, Xue Yang, Xiangyan Tang

Text Classification

Challenge:	Existing work on federated learning for large language models (FL) addresses privacy and data-silo issues in the training of large language model training.
Approach:	They propose a probe-based defense framework for FedLLM that constructs defenses across three levels: Step-Level, Client-Level and Shadow-Level.
Outcome:	The proposed framework improves FedLLM's robustness against malicious clients while maintaining competitive performance on benign data.

Revisiting Data Reconstruction Attacks on Real-world Dataset for Federated Natural Language Understanding (2024.lrec-main)

Copied to clipboard

Zhuo Zhang, Jintao Huang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Yue Yu, Qifan Wang, Lizhen Qu, Zenglin Xu

Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models (2025.findings-acl)

Challenge:	Existing DRA methods fail to accurately recover the original text of real-world privacy data.
Approach:	They propose to use a real-world privacy dataset to examine the performance of federated learning (FL) methods.
Outcome:	The proposed method improves on a real-world privacy dataset and shows that the tokens within a recovery sentence are disordered and intertwined with tokens from other sentences in the same training batch.

Copied to clipboard

Sajjad Ghiasvand, Yifan Yang, Zhiyu Xue, Mahnoosh Alizadeh, Zheng Zhang, Ramtin Pedarsani

Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) excel in translation and summarization due to the capabilities of transformer architectures.
Approach:	They propose to integrate tensorized adapters into model encoder/decoder blocks to improve model adaptability against data heterogeneity.
Outcome:	Experiments on large-scale cross-device FL and large-silo FL show that the proposed methods perform on par or even better than existing federated PEFT approaches while reducing communication cost.

Copied to clipboard

Wenkai Guo, Xuefeng Liu, Haolin Wang, Jianwei Niu, Shaojie Tang, Jing Yuan

FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems (2026.findings-acl)

Challenge:	federated learning (FL) fine-tunes large language models with local data, but organizations are reluctant to share local data.
Approach:	They propose a framework for fine-tuning large language models with local data . they propose centralized fine- tuning with local datasets is a good idea .
Outcome:	The proposed framework allows clients to retain local data while sharing only model parameters for training.

Copied to clipboard

WenHao Wang, Haoting Shi, Mengying Yuan, Yiquan Lin, Panrong Tong, Hanzhang Zhou, Guangyi Liu, Pengxiang Zhao, Yue Wang, Siheng Chen

FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data (2026.acl-long)

Challenge:	a lack of benchmarks capture real-world, cross-platform heterogeneity in GUI training . traditional methods to train GUI agents rely on centralized data collection and manual labeling .
Approach:	They propose a benchmark for developing and evaluating federated GUI agents across mobile, web and desktop platforms.
Outcome:	The proposed benchmarks show that cross-platform collaboration improves performance and identify platform and OS as the most influential factors.

Copied to clipboard

Nuredin Ali Abdelkadir, Anjali Ratnam, Zeerak Talat, Stevie Chancellor

SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs (2026.acl-long)

Challenge:	Social media text data is often used to train machine learning models to identify users exhibiting high-risk mental health behaviors.
Approach:	They apply federatedlearning and Differentially Private FL to two widely-studied mental health prediction tasks using social media text data.
Outcome:	The proposed methods achieve comparable performance to centralized training on depression identification, but have a large performance-privacy trade-off even with low levels of noise.

Copied to clipboard

Mohamed Shaaban, Mohamed Elmahallawy