Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing news recommendation methods rely on centralized storage of user click behavior data, which may lead to privacy concerns and hazards. |
| Approach: | They propose a federated learning framework for privacy-preserving news recommendation . they propose aggregation of news representations and user model by a client . |
| Outcome: | The proposed framework reduces computation and communication cost on clients while keeping promising model performance. |
Similar Papers
Privacy-Preserving News Recommendation Model Learning (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. |
| Approach: | They propose a privacy-preserving method where user behavior data is locally stored on user devices to train accurate news recommendation models. |
| Outcome: | The proposed method can train accurate news recommendation models without centralized storage of user behavior data. |
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing news recommendation methods rely on user behavior data to model user interests and user interests. |
| Approach: | They propose a unified news recommendation framework that uses user data locally stored in user clients to train models and serve users in a privacy-preserving way. |
| Outcome: | The proposed framework outperforms baseline methods and effectively protects user privacy. |
A Federated Framework for LLM-based Recommendation (2025.findings-naacl)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework achieves a more balanced client performance and improved overall performance in a computational and storage-efficient way while safeguarding user privacy well. |
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model (2024.emnlp-main)
Copied to clipboard
| 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. |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)
Copied to clipboard
| 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. |
Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation (2025.findings-emnlp)
Copied to clipboard
| 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. |
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA (2024.findings-emnlp)
Copied to clipboard
Zhu JianHao, Changze Lv, Xiaohua Wang, Muling Wu, Wenhao Liu, Tianlong Li, Zixuan Ling, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing federated learning frameworks require substantial data and computational resources to develop large language models. |
| Approach: | They propose a method that distributes a quantized version of the model’s parameters during training and combine it with a popular fine-tuning method to significantly reduce communication costs. |
| Outcome: | The proposed method enables accurate estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. |
FedPerC: Federated Learning for Language Generation with Personal and Context Preference Embeddings (2023.findings-eacl)
Copied to clipboard
| Challenge: | federated learning is a decentralized learning paradigm that assumes no access to a large labeled dataset and instead leverages averaged parameter updates across all users of the system. |
| Approach: | They propose a method to personalize federated learning with personal embeddings and shared context embeddables. |
| Outcome: | The proposed approach achieves 50% improvement in test-time perplexity using 0.001% of the memory required by baseline approaches and greater sample- and compute-efficiency. |
A Secure and Efficient Federated Learning Framework for NLP (2021.emnlp-main)
Copied to clipboard
Chenghong Wang, Jieren Deng, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding
| 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. |
Privacy-Preserving Federated Learning for Hate Speech Detection (2025.naacl-srw)
Copied to clipboard
| Challenge: | a federated learning system with differential privacy is tailored to low-resource languages . data with fewer than 20 sentences per client struggled due to excessive noise . |
| Approach: | They propose a federated learning system with differential privacy for hate speech detection . they fine-tuned pre-trained language models to find it to be the most effective . |
| Outcome: | The proposed learning system outperforms other models in low-resource languages . balanced datasets and augmenting hateful data with non-hateful examples proved critical . |