FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
This work addresses the need for privacy-preserving, decentralized learning in NLP, offering a standardized benchmark for researchers, though it is incremental as it builds on existing FL and NLP methods.
The authors tackled the lack of systematic evaluation of federated learning methods for NLP tasks by introducing FedNLP, a benchmarking framework that provides empirical comparisons across four task formulations under non-IID data settings.
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.