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arXiv · 2504.19602

Soft-Label Caching and Sharpening for Communication-Efficient Federated Distillation

Kitsuya Azuma·Takayuki Nishio·Yuichi Kitagawa·Wakako Nakano·Takahito Tanimura
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Abstract

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, i.e., normalized probability distributions) 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 competitive accuracy. Enhanced ERA resolves the fundamental instability of conventional temperature-based aggregation, ensuring robust control and high 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 at https://github.com/kitsuyaazuma/SCARLET.

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Kitsuya Azuma, Takayuki Nishio, Yuichi Kitagawa, Wakako Nakano, Takahito Tanimura. 2026-01-30. Soft-Label Caching and Sharpening for Communication-Efficient Federated Distillation. https://doi.org/10.1109/tmc.2026.3652819

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