PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning
PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning
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DOI：
10.1109/itw55543.2023.10161691
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Michail Gkagkos;K. Narayanan;J. Chamberland;C. Georghiades
中科院分区：
文献类型：
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作者：
Michail Gkagkos;K. Narayanan;J. Chamberland;C. Georghiades
我们探索了一种方案，该方案能够在加性白色高斯噪声通道上以联合学习配置训练深度神经网络。我们的目标是创建一个低复杂度，线性压缩策略，称为PolarAir，减少在用户端的梯度的大小，以降低传输它所需的信道使用的数量。建议的方法属于家庭的压缩传感技术，但它构造的传感矩阵和使用多址技术的恢复过程。仿真结果表明，与不压缩的情况下传输梯度相比，它可以将信道使用次数减少约30%。与文献中的其他方案相比，该方案的主要优点是其时间复杂度低。我们还研究了梯度更新的行为和PolarAir在整个训练过程中的性能，以了解如何最好地构建基于压缩感知的压缩方案。
We explore a scheme that enables the training of a deep neural network in a Federated Learning configuration over an additive white Gaussian noise channel. The goal is to create a low complexity, linear compression strategy, called PolarAir, that reduces the size of the gradient at the user side to lower the number of channel uses needed to transmit it. The suggested approach belongs to the family of compressed sensing techniques, yet it constructs the sensing matrix and the recovery procedure using multiple access techniques. Simulations show that it can reduce the number of channel uses by ∼30% when compared to conveying the gradient without compression. The main advantage of the proposed scheme over other schemes in the literature is its low time complexity. We also investigate the behavior of gradient updates and the performance of PolarAir throughout the training process to obtain insight on how best to construct this compression scheme based on compressed sensing.