Federated Learning (FL) is the privacy-preserving machine learning paradigm which collaboratively trains a model across millions of devices. Simulated environments are fundamental to large-scale FL research, allowing researchers to quickly test new ideas to solve system and statistical heterogeneity issues. This work proposes \emph{Pollen}, a novel resource-aware system capable of speeding up FL simulations by efficiently placing clients across distributed and heterogeneous hardware. We propose minimising server-GPU communication and using an efficient client placement policy based on the inherent trade-offs of FL client placement on heterogeneous GPUs. These trade-offs are explored experimentally. This exploration has been conducted via relevant baselines on three popular FL tasks: image classification, speech recognition and text generation. We compare \emph{Pollen} to existing ad-hoc FL frameworks, such as Flower, Flute and FedScale, and show performance gains of $50\%$ to $400\%$.

翻译：联邦学习（FL）是一种隐私保护的机器学习范式，可在数百万设备间协同训练模型。仿真环境是大规模FL研究的基础，使研究人员能够快速测试解决系统与统计异构性问题的新思路。本文提出一种新型资源感知系统Pollen，通过高效地跨分布式异构硬件放置客户端来加速FL仿真。我们提出最小化服务器-GPU通信，并基于FL客户端在异构GPU上放置的内在权衡，采用高效的客户端放置策略。这些权衡通过实验进行了探索。我们在三个主流FL任务（图像分类、语音识别与文本生成）上，基于相关基线进行了探索性实验。我们将Pollen与现有即用型FL框架（如Flower、Flute与FedScale）进行比较，结果表明性能提升达50%至400%。