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Federated Learning Meets Natural Language Processing: A Survey

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arxiv 2107.12603 v1 pith:WUFWBMFE submitted 2021-07-27 cs.CL cs.AIcs.DC

Ming Liu , Stella Ho , Mengqi Wang , Longxiang Gao , Yuan Jin , He Zhang This is my paper
classification cs.CLcs.AIcs.DC
keywords learningfederatedlanguagemodelschallengesdatanaturalprocessing
verification ladder T0 review T1 audit T2 compute T3 formal
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Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy. Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models. However, both big deep neural and language models are trained with huge amounts of data which often lies on the server side. Since text data is widely originated from end users, in this work, we look into recent NLP models and techniques which use federated learning as the learning framework. Our survey discusses major challenges in federated natural language processing, including the algorithm challenges, system challenges as well as the privacy issues. We also provide a critical review of the existing Federated NLP evaluation methods and tools. Finally, we highlight the current research gaps and future directions.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection

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  2. Efficient Federated Learning with Timely Update Dissemination

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    FedASMU and FedSSMU improve federated learning accuracy and speed by dynamically disseminating fresh global models to devices during local training, using server-side and device-side adaptive weighting.

  3. Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission

    cs.LG 2025-06 reject novelty 4.0 of 10

    FedHLM uses federated learning to learn token-level uncertainty thresholds that decide when to offload tokens from a small edge LM to a large cloud LM, claiming a 95 percent reduction in LLM transmissions.

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