skip to main content

Title: FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
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.
; ; ; ; ; ; ; ; ;
Award ID(s):
1813877 1846369
Publication Date:
Journal Name:
Findings of NAACL
Sponsoring Org:
National Science Foundation
More Like this
  1. Providing privacy protection has been one of the primary motivations of Federated Learning (FL). Recently, there has been a line of work on incorporating the formal privacy notion of differential privacy with FL. To guarantee the client-level differential privacy in FL algorithms, the clients’ transmitted model updates have to be clipped before adding privacy noise. Such clipping operation is substantially different from its counterpart of gradient clipping in the centralized differentially private SGD and has not been well-understood. In this paper, we first empirically demonstrate that the clipped FedAvg can perform surprisingly well even with substantial data heterogeneity when training neural networks, which is partly because the clients’ updates become similar for several popular deep architectures. Based on this key observation, we provide the convergence analysis of a differential private (DP) FedAvg algorithm and highlight the relationship between clipping bias and the distribution of the clients’ updates. To the best of our knowledge, this is the first work that rigorously investigates theoretical and empirical issues regarding the clipping operation in FL algorithms.
  2. The conventional machine learning (ML) and deep learning (DL) methods use large amount of data to construct desirable prediction models in a central fusion center for recognizing human activities. However, such model training encounters high communication costs and leads to privacy infringement. To address the issues of high communication overhead and privacy leakage, we employed a widely popular distributed ML technique called Federated Learning (FL) that generates a global model for predicting human activities by combining participated agents’ local knowledge. The state-of-the-art FL model fails to maintain acceptable accuracy when there is a large number of unreliable agents who can infuse false model, or, resource-constrained agents that fails to perform an assigned computational task within a given time window. We developed an FL model for predicting human activities by monitoring agent’s contributions towards model convergence and avoiding the unreliable and resource-constrained agents from training. We assign a score to each client when it joins in a network and the score is updated based on the agent’s activities during training. We consider three mobile robots as FL clients that are heterogeneous in terms of their resources such as processing capability, memory, bandwidth, battery-life and data volume. We consider heterogeneous mobile robotsmore »for understanding the effects of real-world FL setting in presence of resource-constrained agents. We consider an agent unreliable if it repeatedly gives slow response or infuses incorrect models during training. By disregarding the unreliable and weak agents, we carry-out the local training of the FL process on selected agents. If somehow, a weak agent is selected and started showing straggler issues, we leverage asynchronous FL mechanism that aggregate the local models whenever it receives a model update from the agents. Asynchronous FL eliminates the issue of waiting for a long time to receive model updates from the weak agents. To the end, we simulate how we can track the behavior of the agents through a reward-punishment scheme and present the influence of unreliable and resource-constrained agents in the FL process. We found that FL performs slightly worse than centralized models, if there is no unreliable and resource-constrained agent. However, as the number of malicious and straggler clients increases, our proposed model performs more effectively by identifying and avoiding those agents while recognizing human activities as compared to the stateof-the-art FL and ML approaches.« less
  3. Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server. Most existing FL algorithms require models of identical architecture to be deployed across the clients and server, making it infeasible to train large models due to clients' limited system resources. In this work, we propose a novel ensemble knowledge transfer method named Fed-ET in which small models (different in architecture) are trained on clients, and used to train a larger model at the server. Unlike in conventional ensemble learning, in FL the ensemble can be trained on clients' highly heterogeneous data. Cognizant of this property, Fed-ET uses a weighted consensus distillation scheme with diversity regularization that efficiently extracts reliable consensus from the ensemble while improving generalization by exploiting the diversity within the ensemble. We show the generalization bound for the ensemble of weighted models trained on heterogeneous datasets that supports the intuition of Fed-ET. Our experiments on image and language tasks show that Fed-ET significantly outperforms other state-of-the-art FL algorithms with fewer communicated parameters, and is also robust against high data-heterogeneity.
  4. Federated Learning (FL) is a promising framework for multiple clients to learn a joint model without directly sharing the data. In addition to high utility of the joint model, rigorous privacy protection of the data and communication efficiency are important design goals. Many existing efforts achieve rigorous privacy by ensuring differential privacy for intermediate model parameters, however, they assume a uniform privacy parameter for all the clients. In practice, different clients may have different privacy requirements due to varying policies or preferences. In this paper, we focus on explicitly modeling and leveraging the heterogeneous privacy requirements of different clients and study how to optimize utility for the joint model while minimizing communication cost. As differentially private perturbations affect the model utility, a natural idea is to make better use of information submitted by the clients with higher privacy budgets (referred to as "public" clients, and the opposite as "private" clients). The challenge is how to use such information without biasing the joint model. We propose P rojected F ederated A veraging (PFA), which extracts the top singular subspace of the model updates submitted by "public" clients and utilizes them to project the model updates of "private" clients before aggregating them.more »We then propose communication-efficient PFA+, which allows "private" clients to upload projected model updates instead of original ones. Our experiments verify the utility boost of both algorithms compared to the baseline methods, whereby PFA+ achieves over 99% uplink communication reduction for "private" clients.« less
  5. Recent years have seen the increasing attention and popularity of federated learning (FL), a distributed learning framework for privacy and data security. However, by its fundamental design, federated learning is inherently vulnerable to model poisoning attacks: a malicious client may submit the local updates to influence the weights of the global model. Therefore, detecting malicious clients against model poisoning attacks in federated learning is useful in safety-critical tasks.However, existing methods either fail to analyze potential malicious data or are computationally restrictive. To overcome these weaknesses, we propose a robust federated learning method where the central server learns a supervised anomaly detector using adversarial data generated from a variety of state-of-the-art poisoning attacks. The key idea of this powerful anomaly detector lies in a comprehensive understanding of the benign update through distinguishing it from the diverse malicious ones. The anomaly detector would then be leveraged in the process of federated learning to automate the removal of malicious updates (even from unforeseen attacks).Through extensive experiments, we demonstrate its effectiveness against backdoor attacks, where the attackers inject adversarial triggers such that the global model will make incorrect predictions on the poisoned samples. We have verified that our method can achieve 99.0% detection AUCmore »scores while enjoying longevity as the model converges. Our method has also shown significant advantages over existing robust federated learning methods in all settings. Furthermore, our method can be easily generalized to incorporate newly-developed poisoning attacks, thus accommodating ever-changing adversarial learning environments.« less