Federated Learning is an evolving machine learning paradigm, in which multiple clients perform computations based on their individual private data, interspersed by communication with a remote server. A common strategy to curtail communication costs is Local Training, which consists in performing multiple local stochastic gradient descent steps between successive communication rounds. However, the conventional approach to local training overlooks the practical necessity for client-specific personalization, a technique to tailor local models to individual needs. We introduce Scafflix, a novel algorithm that efficiently integrates explicit personalization with local training. This innovative approach benefits from these two techniques, thereby achieving doubly accelerated communication, as we demonstrate both in theory and practice.

翻译：联邦学习是一种不断发展的机器学习范式，其中多个客户端基于各自的私有数据执行计算，并与远程服务器进行间断性通信。减少通信成本的常见策略是本地训练，即在连续通信轮次之间执行多次本地随机梯度下降步骤。然而，传统的本地训练方法忽视了客户端特定个性化的实际需求——这是一种针对个体需求定制本地模型的技术。我们提出了Scafflix，一种将显式个性化与本地训练高效集成的新算法。这种创新方法得益于这两种技术，从而实现了双重加速通信，我们在理论和实践中均证明了这一点。