Federated Learning (FL) enables collaborative model training across decentralized clients, enhancing privacy by keeping data local. Yet conventional FL, relying on frequent parameter-sharing, suffers from high communication overhead and limited model heterogeneity. Distillation-based FL approaches address these issues by sharing predictions (soft-labels) instead, but they often involve redundant transmissions across communication rounds, reducing efficiency. We propose SCARLET, a novel framework integrating synchronized soft-label caching and an enhanced Entropy Reduction Aggregation (Enhanced ERA) mechanism. SCARLET minimizes redundant communication by reusing cached soft-labels, achieving up to 50% reduction in communication costs compared to existing methods while maintaining accuracy. Enhanced ERA can be tuned to adapt to non-IID data variations, ensuring robust aggregation and performance in diverse client scenarios. Experimental evaluations demonstrate that SCARLET consistently outperforms state-of-the-art distillation-based FL methods in terms of accuracy and communication efficiency. The implementation of SCARLET is publicly available atthis https URL.
View on arXiv@article{azuma2025_2504.19602,
title={ Soft-Label Caching and Sharpening for Communication-Efficient Federated Distillation },
author={ Kitsuya Azuma and Takayuki Nishio and Yuichi Kitagawa and Wakako Nakano and Takahito Tanimura },
journal={arXiv preprint arXiv:2504.19602},
year={ 2025 }
}