Federated Learning with Noisy User Feedback (2022)

Abstract: Machine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. Thishas led to growing concerns over user privacy, since human interaction data typically needs to be transmitted to the cloud in order to trainand improve such systems. Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy. However, since ML models are most commonly trained with label supervision, we need a way to extract labels on edge to make FL viable. In this work, we propose a strategy for training FL models using positive and negative user feedback. We also design a novel framework to study different noise patterns in user feedback, and explore how well standard noise-robust objectives can help mitigate this noise when training models in a federated setting. We evaluate our proposed training setup through detailed experiments on two text classification datasets and analyze the effects of varying levels of user reliability and feedback noise on model performance. We show that our method improves substantially over a self-training baseline, achieving performance closer to models trained with full supervision.

## introduction artificial intelligence (ai) and machine learning (ml) have become increasingly common in modern society with applications ranging from simple standalone products to complex modules @xcite . however, this rise has also created growing privacy concerns @xcite ; @xcite . such concerns may affect user willingness the adapt new technologies @xcite . in response, many government agencies across the world have introduced regulations to protect the data-handling rights of their * equal contributions citizens, such as the european union's gdpr general data protection regulation and california's ccpa california consumer privacy act. federated learning (fl) is a step in this direction to improve consumer trust, where models are trained without moving data out of client devices. the typical fl approach is to iteratively train local models on edge devices and then propagate them back to a central node in order to update the global model. most commonly, this is done using federated averaging (fedavg) @xcite , where we take a simple average over the client model parameters. however, in order to update local models on the edge, this setup assumes the presence of labeled user data on each device, which is often not possible. most prior works do not address this problem, but simulate fully-supervised federated learning by distributing existing labeled datasets across edge devices. in this work, we consider a more realistic scenario, where labels are not available on device. rather than turning to unsupervised learning as seen in @xcite , we instead propose a novel setup to leverage user feedback in order to train the fl model. in many real world ai applications with direct or indirect human interaction, it is possible to collect either explicit user feedback (e.g., using a voice or a screen-based prompt) or implicit feedback in the form of behavioral cues. for an example of implicit feedback, consider a user interacting with a virtual ai assistant (such as alexa), who asks to play the song 'bohemian rhapsody' from the band queen. the virtual assistant would interpret the prompt and select a song from its library to play. if the user lets the music play without interruption, this can be viewed as positive feedback, suggesting that the underlying model interpreted the request correctly. on the other hand, if the user interrupts the music and makes a repeat (or different) request, this can be viewed as negative feedback, suggesting that the underlying model prediction was incorrect. in this work, we propose to leverage such feedback in federated model training. ## modeling user feedback we propose a general framework for leveraging user feedback in federated learning. we use text classification as an exemplar task to evaluate this framework, but the proposed setup can be easily applied to other tasks. we use two benchmark text classification datasets: the stanford sentiment treebank dataset (sst2) and the 20 newsgroups dataset (20news). the sst2 dataset comprises of 11, 855 phrases from movie reviews and the corresponding binary sentiment labels. the 20 newsgroup dataset 20news consists of 18k news articles and headers, organized into 20 classes. ## conclusion in this work, we propose a novel framework for federated learning which leverages noisy user feedback on the edge. our framework eliminates the need for labeling edge data, in turn improving customer privacy since we no longer need to move data off of edge devices for annotation. in order to evaluate our framework, we propose a method to simulate user feedback on publicly-available datasets and a parametric model to simulate user noise in that feedback. using that method, we conduct experiments on two benchmark text classification datasets and show that models trained with our framework significantly improve over self-training baselines. future work includes deploying our framework in a real world fl application and incorporating live user feedback in model training. we can also explore other noise-robustness strategies for low and medium label-noise scenarios. one such strategy would be to incorporate a measure of user reliability into the calculation of each new global model in fedavg -e.g. the updated global model parameters could be computed as a weighted average of client models, with the weight capturing some measure of each client's reliability.