Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta
| 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. |
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| 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 . |
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FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data (2026.acl-long)
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| 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. |
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Safely Learning with Private Data: A Federated Learning Framework for Large Language Model (2024.emnlp-main)
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| 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. |
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Safe-FedLLM: Delving into the Safety of Federated Large Language Models (2026.acl-long)
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| 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. |
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Tunable Soft Prompts are Messengers in Federated Learning (2023.findings-emnlp)
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| Challenge: | Existing methods to protect model privacy in federated learning (FL) are limited. |
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A Federated Framework for LLM-based Recommendation (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated potential in building generative recommendation systems through fine-tuning user behavior data. |
| Approach: | They propose a federated framework for LLM-based recommendation that combines dynamic parameter aggregation and learning speed for different clients. |
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Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
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Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)
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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang
| Challenge: | Existing approaches to reduce label noise rely on heuristics and sample losses. |
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Federated Data-Efficient Instruction Tuning for Large Language Models (2025.findings-acl)
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| 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. |
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FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023.findings-acl)
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| 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 . |
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