Relaxed Contrastive Learning for Federated Learning

Seonguk Seo, Jinkyu Kim, Geeho Kim, Bohyung Han

Keywords: dimensional collapse, transferability, federated learning, local deviation

TL;DR: Dimensional collapse and transferability are crucial keys to address federated learning and local gradient deviations.

Abstract: We propose a novel contrastive learning framework to effectively address the challenges of data heterogeneity in federated learning. We first analyze the inconsistency of gradient updates across clients during local training and establish its dependence on the distribution of feature representations, leading to the derivation of the supervised contrastive learning (SCL) objective to mitigate local deviations. In addition, we show that a naïve integration of SCL into federated learning incurs representation collapse, resulting in slow convergence and limited performance gains. To address this issue, we introduce a relaxed contrastive learning loss that imposes a divergence penalty on excessively similar sample pairs within each class. This strategy prevents collapsed representations and enhances feature transferability, facilitating collaborative training and leading to significant performance improvements. Our framework outperforms all existing federated learning approaches by significant margins on the standard benchmarks, as demonstrated by extensive experimental results.

Submission Number: 11