Adversarial Collaborative Learning on Non-IID FeaturesDownload PDF

Published: 01 Feb 2023, 19:30, Last Modified: 13 Feb 2023, 23:29Submitted to ICLR 2023Readers: Everyone
Keywords: Federated Learning, Collaborative Learning
TL;DR: The paper proposes a new collaborative learning framework on non-IID features.
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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