  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 na\"ive
adoption of SCL in federated learning leads to 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 huge margins on the standard benchmarks through
extensive experimental results.
