基于数据相似性的一次性聚类，提升多任务联邦学习分组效率
Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning
- 通过数据相似性实现用户一次性聚类，无需先验知识
- 在CIFAR-10和Fashion MNIST上准确率提升，方差降低
- 适合隐私敏感、任务异构的分布式学习场景
针对分层联邦学习中用户执行不同任务导致的聚类身份估计难题，本文提出一种基于数据相似性的单次聚类算法。该方法能有效识别并分组具有相同任务的用户，在共享特征提取层权重的同时保持组间协作。相比基线，本方法在CIFAR-10和Fashion MNIST等数据集上显著提升模型准确率并降低方差，同时克服了隐私泄露、通信开销大及对模型或损失函数行为未知等挑战。
原文摘要 · Abstract (English)
We address the problem of cluster identity estimation in a hierarchical federated learning setting in which users work toward learning different tasks. To overcome the challenge of task heterogeneity, users need to be grouped in a way such that users with the same task are in the same group, conducting training together, while sharing the weights of feature extraction layers with the other groups. Toward that end, we propose a one-shot clustering algorithm that can effectively identify and group users based on their data similarity. This enables more efficient collaboration and sharing of a common layer representation within the federated learning system. Our proposed algorithm not only enhances the clustering process, but also overcomes challenges related to privacy concerns, communication overhead, and the need for prior knowledge about learning models or loss function behaviors. We validate our proposed algorithm using various datasets such as CIFAR-10 and Fashion MNIST, and show that it outperforms the baseline in terms of accuracy and variance reduction.
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