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Increasing concerns and regulations about data privacy and sparsitynecessitate the study of privacy-preserving, decentralized learning methods fornatural language processing (NLP) tasks. Federated learning (FL) providespromising approaches for a large number of clients (e.g., personal devices ororganizations) to collaboratively learn a shared global model to benefit allclients while allowing users to keep their data locally. Despite interest instudying FL methods for NLP tasks, a systematic comparison and analysis islacking in the literature. Herein, we present the FedNLP, a benchmarkingframework for evaluating federated learning methods on four different taskformulations: text classification, sequence tagging, question answering, andseq2seq. We propose a universal interface between Transformer-based languagemodels (e.g., BERT, BART) and FL methods (e.g., FedAvg, FedOPT, etc.) undervarious non-IID partitioning strategies. Our extensive experiments with FedNLPprovide empirical comparisons between FL methods and helps us better understandthe inherent challenges of this direction. The comprehensive analysis points tointriguing and exciting future research aimed at developing FL methods for NLPtasks.

FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks