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Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
arXiv - CS - Information Theory Pub Date : 2020-11-20 , DOI: arxiv-2011.10282 Hang Liu, Xiaojun Yuan, Ying-Jun Angela Zhang
arXiv - CS - Information Theory Pub Date : 2020-11-20 , DOI: arxiv-2011.10282 Hang Liu, Xiaojun Yuan, Ying-Jun Angela Zhang
To exploit massive amounts of data generated at mobile edge networks,
federated learning (FL) has been proposed as an attractive substitute for
centralized machine learning (ML). By collaboratively training a shared
learning model at edge devices, FL avoids direct data transmission and thus
overcomes high communication latency and privacy issues as compared to
centralized ML. To improve the communication efficiency in FL model
aggregation, over-the-air computation has been introduced to support a large
number of simultaneous local model uploading by exploiting the inherent
superposition property of wireless channels. However, due to the heterogeneity
of communication capacities among edge devices, over-the-air FL suffers from
the straggler issue in which the device with the weakest channel acts as a
bottleneck of the model aggregation performance. This issue can be alleviated
by device selection to some extent, but the latter still suffers from a
tradeoff between data exploitation and model communication. In this paper, we
leverage the reconfigurable intelligent surface (RIS) technology to relieve the
straggler issue in over-the-air FL. Specifically, we develop a learning
analysis framework to quantitatively characterize the impact of device
selection and model aggregation error on the convergence of over-the-air FL.
Then, we formulate a unified communication-learning optimization problem to
jointly optimize device selection, over-the-air transceiver design, and RIS
configuration. Numerical experiments show that the proposed design achieves
substantial learning accuracy improvements compared with the state-of-the-art
approaches, especially when channel conditions vary dramatically across edge
devices.
中文翻译:
可重新配置的智能表面启用联合学习:统一的交流学习设计方法
为了利用在移动边缘网络上生成的大量数据,已经提出了联合学习(FL)作为集中式机器学习(ML)的有吸引力的替代方法。与集中式ML相比,通过在边缘设备上共同训练共享学习模型,FL避免了直接数据传输,从而克服了高通信延迟和隐私问题。为了提高FL模型聚合中的通信效率,已经引入了无线计算,以通过利用无线信道的固有叠加特性来支持大量同时进行的本地模型上传。但是,由于边缘设备之间通信能力的异质性,空中FL遭受了杂散问题的困扰,其中通道最弱的设备成为模型聚合性能的瓶颈。通过设备选择可以在某种程度上缓解此问题,但后者仍会遭受数据开发和模型通信之间的折衷。在本文中,我们利用可重构智能表面(RIS)技术来缓解空中FL中的散乱问题。具体来说,我们开发了一种学习分析框架,以定量表征设备选择和模型聚合误差对空中FL收敛的影响。然后,我们制定统一的通信学习优化问题,以共同优化设备选择,空中收发器设计和RIS配置。数值实验表明,与最先进的方法相比,所提出的设计在学习准确性上有了很大的提高,
更新日期:2020-11-23
中文翻译:
可重新配置的智能表面启用联合学习:统一的交流学习设计方法
为了利用在移动边缘网络上生成的大量数据,已经提出了联合学习(FL)作为集中式机器学习(ML)的有吸引力的替代方法。与集中式ML相比,通过在边缘设备上共同训练共享学习模型,FL避免了直接数据传输,从而克服了高通信延迟和隐私问题。为了提高FL模型聚合中的通信效率,已经引入了无线计算,以通过利用无线信道的固有叠加特性来支持大量同时进行的本地模型上传。但是,由于边缘设备之间通信能力的异质性,空中FL遭受了杂散问题的困扰,其中通道最弱的设备成为模型聚合性能的瓶颈。通过设备选择可以在某种程度上缓解此问题,但后者仍会遭受数据开发和模型通信之间的折衷。在本文中,我们利用可重构智能表面(RIS)技术来缓解空中FL中的散乱问题。具体来说,我们开发了一种学习分析框架,以定量表征设备选择和模型聚合误差对空中FL收敛的影响。然后,我们制定统一的通信学习优化问题,以共同优化设备选择,空中收发器设计和RIS配置。数值实验表明,与最先进的方法相比,所提出的设计在学习准确性上有了很大的提高,