Mass data traffics, low-latency wireless services and advanced artificial intelligence (AI) technologies have driven the emergence of a new paradigm for wireless networks, namely edge-intelligent networks, which are more efficient and flexible than traditional cloud-intelligent networks. Considering users’ privacy, model sharing-based federated learning (FL) that migrates model parameters but not private data from edge devices to a central cloud is particularly attractive for edge-intelligent networks. Due to multiple rounds of iterative updating of high-dimensional model parameters between base station (BS) and edge devices, the communication reliability is a critical issue of FL for edge-intelligent networks. We reveal the impacts of the errors generated during model broadcast and model aggregation via wireless channels caused by channel fading, interference and noise on the accuracy of FL, especially when there exists channel uncertainty. To alleviate the impacts, we propose a robust FL algorithm for edge-intelligent networks with channel uncertainty, which is formulated as a worst-case optimization problem with joint device selection and transceiver design. Finally, simulation results validate the robustness and effectiveness of the proposed algorithm.

中文翻译：

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边缘智能网络联合学习的鲁棒设计

海量数据流量、低延迟无线服务和先进的人工智能 (AI) 技术推动了无线网络新范式的出现，即边缘智能网络，它比传统的云智能网络更高效、更灵活。考虑到用户的隐私，将模型参数而非私有数据从边缘设备迁移到中央云的基于模型共享的联邦学习 (FL) 对于边缘智能网络特别有吸引力。由于基站（BS）和边缘设备之间高维模型参数的多轮迭代更新，通信可靠性是边缘智能网络FL的关键问题。我们揭示了由信道衰落引起的模型广播和模型聚合过程中产生的错误的影响，干扰和噪声对 FL 精度的影响，尤其是在存在信道不确定性的情况下。为了减轻影响，我们针对具有信道不确定性的边缘智能网络提出了一种鲁棒的 FL 算法，该算法被表述为具有联合设备选择和收发器设计的最坏情况优化问题。最后，仿真结果验证了所提算法的鲁棒性和有效性。