Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
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DOI：
10.1109/twc.2023.3297790
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发表时间：
2024-03
影响因子：
10.4
通讯作者：
Sihua Wang;Mingzhe Chen;Christopher G. Brinton;Changchuan Yin;W. Saad;Shuguang Cui
中科院分区：
文献类型：
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作者：
Sihua Wang;Mingzhe Chen;Christopher G. Brinton;Changchuan Yin;W. Saad;Shuguang Cui
研究了通过模型量化提高联邦学习无线通信和计算效率的问题。在所提出的位宽FL方案中，边缘设备训练它们的本地FL模型参数的量化版本并将其发送到协调服务器，协调服务器进而将它们聚合成量化的全局模型并对设备进行重新配置。目标是联合确定用于局部FL模型量化的位宽和在每次迭代时参与FL训练的设备集合。我们将其作为一个优化问题，旨在最大限度地减少每次迭代设备采样预算和延迟要求下量化FL的训练损失。然而，如果没有（i）对量化如何影响全局ML性能的具体理解以及（ii）服务器有效构建此过程估计的能力，则很难解决公式化的问题。为了解决第一个挑战，我们分析了有限的无线资源和诱导的量化误差如何影响所提出的FL方法的性能。我们的研究结果量化了两次连续迭代之间FL训练损失的改善如何取决于设备选择和量化方案以及正在学习的模型所固有的几个参数。然后，为了解决第二个挑战，我们证明了FL训练过程可以描述为马尔可夫决策过程（MDP），并提出了一种基于模型的强化学习（RL）方法来优化迭代中的动作选择。与无模型RL相比，这种基于模型的RL方法利用FL训练过程的导出的数学表征来发现有效的设备选择和量化方案，而不施加额外的设备通信开销。仿真结果表明，所提出的FL算法可以减少29%和63%的收敛时间相比，无模型RL方法和标准FL方法，分别。
This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. The goal is to jointly determine the bitwidths employed for local FL model quantization and the set of devices participating in FL training at each iteration. We pose this as an optimization problem that aims to minimize the training loss of quantized FL under a per-iteration device sampling budget and delay requirement. However, the formulated problem is difficult to solve without (i) a concrete understanding of how quantization impacts global ML performance and (ii) the ability of the server to construct estimates of this process efficiently. To address the first challenge, we analytically characterize how limited wireless resources and induced quantization errors affect the performance of the proposed FL method. Our results quantify how the improvement of FL training loss between two consecutive iterations depends on the device selection and quantization scheme as well as on several parameters inherent to the model being learned. Then, to address the second challenge, we show that the FL training process can be described as a Markov decision process (MDP) and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Simulation results show that the proposed FL algorithm can reduce the convergence time by 29% and 63% compared to a model free RL method and the standard FL method, respectively.