Approximate Wireless Communication for Federated Learning
Approximate Wireless Communication for Federated Learning
复制标题
联邦学习的近似无线通信
DOI：
10.1145/3586209.3591399
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Qian, Y.
中科院分区：
文献类型：
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作者：
Ma, X.;Sun, H.;Hu, R. Q.;Qian, Y.
针对上行链路传输中的联合学习(FL)模型聚合问题，提出了一种近似的无线通信方案。我们考虑了无线网络中FL模型交换过程中暴露比特差错的现实信道。我们的研究表明，模型传输过程中的随机误码会显著影响FL性能。为了克服这一挑战，我们提出了一种基于数学和统计证明的近似通信方案，该方案基于机器学习(ML)模型梯度在一定约束下是有界的。这一界限使我们能够引入一种新的编码方案，用于梯度值的浮点到二进制表示及其QAM星座映射。此外，由于FL梯度具有容错性，因此当信道质量令人满意时，所提出的方案简单地传递带误差的梯度，从而消除了大量的纠错码和/或重传。直接的好处包括更少的开销和更低的延迟。该方案适用于无线网络中资源受限的设备。仿真结果表明，该方案能有效地降低误码对误码性能的影响，在达到相同的学习性能的情况下，比采用纠错和重传的传输节省至少一半的时间。此外，我们还研究了格雷编码在高阶调制中的比特保护机制的有效性，发现这种方法显著地提高了学习性能。
This paper presents an approximate wireless communication scheme for federated learning (FL) model aggregation in the uplink transmission. We consider a realistic channel that reveals bit errors during FL model exchange in wireless networks. Our study demonstrates that random bit errors during model transmission can significantly affect FL performance. To overcome this challenge, we propose an approximate communication scheme based on the mathematical and statistical proof that machine learning (ML) model gradients are bounded under certain constraints. This bound enables us to introduce a novel encoding scheme for float-to-binary representation of gradient values and their QAM constellation mapping. Besides, since FL gradients are error-resilient, the proposed scheme simply delivers gradients with errors when the channel quality is satisfactory, eliminating extensive error-correcting codes and/or retransmission. The direct benefits include less overhead and lower latency. The proposed scheme is well-suited for resource-constrained devices in wireless networks. Through simulations, we show that the proposed scheme is effective in reducing the impact of bit errors on FL performance and saves at least half the time than transmission with error correction and retransmission to achieve the same learning performance. In addition, we investigated the effectiveness of bit protection mechanisms in high-order modulation when gray coding is employed and found that this approach considerably enhances learning performance.
DOI：
--
发表时间：
2022
期刊：
IEEE Communications Letters
影响因子：
--
作者：
M. Shirvanimoghaddam;Ayoob Salari;Yifeng Gao;Aradhika Guha
通讯作者：
Aradhika Guha