论文标题
间歇性客户可用性和随时间变化的沟通限制下的联合学习
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints
论文作者
论文摘要
联合学习系统促进了在可能异质数据分布在大量客户的环境中的全球模型的培训。此类系统在具有间歇性客户端可用性和/或随时间变化的通信约束的设置中运行。结果，由联合学习系统培训的全球模型可能会偏向具有更高可用性的客户。我们提出了F3ast，这是一种无偏的算法，该算法动态地学习了依赖可用性的客户选择策略，该策略渐近地将客户采样差异对全球模型融合的影响最小化，从而提高了联合学习的性能。在通信限制下，在各种环境中针对间歇性可用的客户进行了测试，该算法在综合数据和使用CIFAR100和莎士比亚数据集中实际联合的基准测试实验中证明了其功效。我们比FedAvg表现出高达186％和8％的准确性，在CIFAR100和莎士比亚的Fedadam分别显示出8％和7％。
Federated learning systems facilitate training of global models in settings where potentially heterogeneous data is distributed across a large number of clients. Such systems operate in settings with intermittent client availability and/or time-varying communication constraints. As a result, the global models trained by federated learning systems may be biased towards clients with higher availability. We propose F3AST, an unbiased algorithm that dynamically learns an availability-dependent client selection strategy which asymptotically minimizes the impact of client-sampling variance on the global model convergence, enhancing performance of federated learning. The proposed algorithm is tested in a variety of settings for intermittently available clients under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets. We show up to 186% and 8% accuracy improvements over FedAvg, and 8% and 7% over FedAdam on CIFAR100 and Shakespeare, respectively.
