mlsanityData to 7 Oct 2026

A Secure and Efficient Federated Learning Framework for NLP

arXiv 2201.11934 ↗First posted 28 Jan 2022cs.CR cs.CL cs.LGcited by 9

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Abstract (arXiv)

In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks. Existing solutions either involve a trusted aggregator or require heavyweight cryptographic primitives, which degrades performance significantly. Moreover, many existing secure FL designs work only under the restrictive assumption that none of the clients can be dropped out from the training protocol. To tackle these problems, we propose SEFL, a secure and efficient FL framework that (1) eliminates the need for the trusted entities; (2) achieves similar and even better model accuracy compared with existing FL designs; (3) is resilient to client dropouts. Through extensive experimental studies on natural language processing (NLP) tasks, we demonstrate that the SEFL achieves comparable accuracy compared to existing FL solutions, and the proposed pruning technique can improve runtime performance up to 13.7x.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 17 of its 28 references that are in the map. First posted on arXiv 28 Jan 2022.

Builds on

17 of 28 references.

References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016Pointer Sentinel Mixture Modelscited by 2,992
  4. 2019Deep Leakage from Gradientscited by 1,004
  5. 2019Towards Federated Learning at Scale: System Designcited by 826
  6. 2016Learning Structured Sparsity in Deep Neural Networkscited by 538
  7. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  8. 2018Scalable Private Learning with PATEcited by 266
  9. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  10. 2019Model Compression with Adversarial Robustness: A Unified Optimization Frameworkcited by 71
  11. 2017CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matricescited by 53
  12. 2019PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devicescited by 48
5 more
  1. 2020Against Membership Inference Attack: Pruning is All You Needcited by 32
  2. 2020SAPAG: A Self-Adaptive Privacy Attack From Gradientscited by 27
  3. 2017Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rankcited by 11
  4. 2019DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networkscited by 5
  5. 2020ESMFL: Efficient and Secure Models for Federated Learningcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 2; 2025: 1; 2026: 1.

9 papers.

  1. 2022FedAdapter: Efficient Federated Learning for Modern NLPcited by 24
  2. 2023Delving into the Adversarial Robustness of Federated Learningcited by 19
  3. 2022Federated Few-Shot Learning for Mobile NLPcited by 15
  4. 2024LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classificationcited by 8
  5. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  6. 2023Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clientscited by 2
  7. 2023Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarizationcited by 1
  8. 2025DSFL: A Dual-Server Byzantine-Resilient Federated Learning Framework via Group-Based Secure Aggregationcited by 1
  9. 2026Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTscited by 0