Papers › FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

18 Apr 2021Findings (NAACL) 2022 7arXiv:2104.08815archive 2025-07-28

Bill Yuchen Lin, Chaoyang He, Zihang Zeng, Hulin Wang, Yufen Huang, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr

Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks. Federated learning (FL) provides promising approaches for a large number of clients (e.g., personal devices or organizations) to collaboratively learn a shared global model to benefit all clients while allowing users to keep their data locally. Despite interest in studying FL methods for NLP tasks, a systematic comparison and analysis is lacking in the literature. Herein, we present the FedNLP, a benchmarking framework for evaluating federated learning methods on four different task formulations: text classification, sequence tagging, question answering, and seq2seq. We propose a universal interface between Transformer-based language models (e.g., BERT, BART) and FL methods (e.g., FedAvg, FedOPT, etc.) under various non-IID partitioning strategies. Our extensive experiments with FedNLP provide empirical comparisons between FL methods and helps us better understand the inherent challenges of this direction. The comprehensive analysis points to intriguing and exciting future research aimed at developing FL methods for NLP tasks.

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TYtrack/FedNLP mentioned on GitHubpytorch report

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BenchmarkingFederated LearningLanguage ModellingPrivacy PreservingQuestion AnsweringText Classificationtext-classification

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Absolute Position EncodingsAdamAttentionAttention DropoutBERTBPEDense ConnectionsDropoutLSTMLabel SmoothingLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSeq2SeqSigmoid ActivationSoftmaxTanh ActivationTransformerWeight DecayWordPiece

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