Federated Few-Shot Learning with Adversarial Learning

TL;DR

This paper introduces FedFSL, a federated learning framework for few-shot classification that maintains data privacy and improves performance on vision and language tasks by regularizing client models and using adversarial training.

Contribution

The paper proposes a novel federated few-shot learning framework with adversarial training and divergence regularization to improve unseen class classification.

Findings

Outperforms baselines by over 10% in vision tasks

Achieves more than 5% improvement in language tasks

Effectively handles data scarcity and distribution across devices

Abstract

We are interested in developing a unified machine learning model over many mobile devices for practical learning tasks, where each device only has very few training data. This is a commonly encountered situation in mobile computing scenarios, where data is scarce and distributed while the tasks are distinct. In this paper, we propose a federated few-shot learning (FedFSL) framework to learn a few-shot classification model that can classify unseen data classes with only a few labeled samples. With the federated learning strategy, FedFSL can utilize many data sources while keeping data privacy and communication efficiency. There are two technical challenges: 1) directly using the existing federated learning approach may lead to misaligned decision boundaries produced by client models, and 2) constraining the decision boundaries to be similar over clients would overfit to training tasks but not adapt well to unseen tasks. To address these issues, we propose to regularize local updates by minimizing the divergence of client models. We also formulate the training in an adversarial fashion and optimize the client models to produce a discriminative feature space that can better represent unseen data samples. We demonstrate the intuitions and conduct experiments to show our approaches outperform baselines by more than 10% in learning vision tasks and 5% in language tasks.