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首页> 外文会议>IEEE International Conference on Distributed Computing Systems >Resource-Efficient and Convergence-Preserving Online Participant Selection in Federated Learning
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Resource-Efficient and Convergence-Preserving Online Participant Selection in Federated Learning

机译：在联合学习中的资源高效和融合保留在线参与者选择

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摘要

Federated learning achieves the privacy-preserving training of models on mobile devices by iteratively aggregating model updates instead of raw training data to the server. Since excessive training iterations and model transferences incur heavy usage of computation and communication resources, selecting appropriate devices and excluding unnecessary model updates can help save the resource usage. We formulate an online time-varying non-linear integer program to minimize the cumulative resource usage over time while achieving the desired long-term convergence of the model being trained. We design an online learning algorithm to make fractional control decisions based on both previous system dynamics and previous training results, and also design an online randomized rounding algorithm to convert the fractional decisions into integers without violating any constraints. We rigorously prove that our online approach only incurs sub-linear dynamic regret for the optimality loss and sub-linear dynamic fit for the long-term convergence violation. We conduct extensive trace-driven evaluations and confirm the empirical superiority of our approach over alternative algorithms in terms of up to 27% reduction on the resource usage while sacrificing only 4% reduction on accuracy.
机译：联邦学习达到通过反复聚集模型更新，而不是原始的训练数据发送到服务器的移动设备型号隐私保护培训。由于过度训练迭代和模型流转招致计算与通信资源的大量使用，选择合适的设备，排除不必要的模型更新可以帮助用户节省资源的使用。我们制定一个在线的时变非线性整数规划随着时间的推移减少累积的资源使用率，同时实现了模型的期望长期收敛的培训。我们设计一个在线学习算法，使基于两个以前的系统动力学和以前的训练结果的分数控制决策，并且还设计了在线随机算法取整到小数决定到整数转换不违反任何约束。我们严格证明，我们的在线方法只招了最优损失和子线性动态适合长期收敛违反子线性动态遗憾。我们进行了广泛的跟踪驱动的评估和确认我们对替代算法方法在了对资源的使用减少27％方面的经验优势，而仅仅牺牲4精度的下降％。

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