Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model while keeping their data on-premise. However, the straggler issue, due to slow clients, often hinders the efficiency and scalability of FL. This paper presents FedCore, an algorithm that innovatively tackles the straggler problem via the decentralized selection of coresets, representative subsets of a dataset. Contrary to existing centralized coreset methods, FedCore creates coresets directly on each client in a distributed manner, ensuring privacy preservation in FL. FedCore translates the coreset optimization problem into a more tractable k-medoids clustering problem and operates distributedly on each client. Theoretical analysis confirms FedCore's convergence, and practical evaluations demonstrate an 8x reduction in FL training time, without compromising model accuracy. Our extensive evaluations also show that FedCore generalizes well to existing FL frameworks.

翻译：摘要：联邦学习是一种机器学习范式，允许多个客户端协同训练共享模型，同时将数据保留在本地。然而，由慢速客户端引发的掉队者问题往往阻碍联邦学习的效率和可扩展性。本文提出FedCore算法，该算法通过分布式选择核心集（数据集的代表性子集）创新性地解决了掉队问题。与现有集中式核心集方法不同，FedCore以分布式方式在每个客户端直接构建核心集，从而确保联邦学习中的隐私保护。FedCore将核心集优化问题转化为更易处理的k-medoids聚类问题，并在每个客户端上分布式执行。理论分析验证了FedCore的收敛性，实际评估表明其在不影响模型精度的前提下，将联邦学习训练时间缩短了8倍。广泛评估还显示，FedCore能够良好地泛化到现有联邦学习框架中。