Adversarial Collaborative Learning on Non-IID Features

Qinbin Li, Bingsheng He, Dawn Song

Keywords: Federated Learning, Collaborative Learning

Abstract: Federated Learning (FL) has been a popular approach to enable collaborative learning on multiple parties without exchanging raw data. However, the model performance of FL may degrade a lot due to non-IID data. While many FL algorithms focus on non-IID labels, FL on non-IID features has largely been overlooked. Different from typical FL approaches, the paper proposes a new learning concept called ADCOL (Adversarial Collaborative Learning) for non-IID features. Instead of adopting the widely used model-averaging scheme, ADCOL conducts training in an adversarial way: the server aims to train a discriminator to distinguish the representations of the parties, while the parties aim to generate a common representation distribution. Our experiments on three tasks show that ADCOL achieves better performance than state-of-the-art FL algorithms on non-IID features.

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TL;DR: The paper proposes a new collaborative learning framework on non-IID features.

Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning