论文标题
联合学习中的基于贡献的设备选择方案
A Contribution-based Device Selection Scheme in Federated Learning
论文作者
论文摘要
在联合学习（FL）设置中，许多设备有助于培训通用模型。我们提供了一种选择提供更新的设备，以实现改进的概括，快速收敛和更好的设备级别性能。我们制定了最低 - 最大优化问题，并将其分解为原始偶的设置，其中二元性差距用于量化设备级的性能。我们的策略通过\ emph {exploitation}的随机设备选择，通过简化的设备贡献来结合数据新鲜度。从概括和个性化方面，这都提高了训练有素的模型的性能。在剥削阶段，应用了修改的截短蒙特卡洛（TMC）方法，以估计设备的贡献并降低开销的通信。实验结果表明，所提出的方法具有竞争性能，对基线方案的沟通开销和竞争性个性化绩效较低。
In a Federated Learning (FL) setup, a number of devices contribute to the training of a common model. We present a method for selecting the devices that provide updates in order to achieve improved generalization, fast convergence, and better device-level performance. We formulate a min-max optimization problem and decompose it into a primal-dual setup, where the duality gap is used to quantify the device-level performance. Our strategy combines \emph{exploration} of data freshness through a random device selection with \emph{exploitation} through simplified estimates of device contributions. This improves the performance of the trained model both in terms of generalization and personalization. A modified Truncated Monte-Carlo (TMC) method is applied during the exploitation phase to estimate the device's contribution and lower the communication overhead. The experimental results show that the proposed approach has a competitive performance, with lower communication overhead and competitive personalization performance against the baseline schemes.
