Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the communication efficiency of FL, over-the-air computation allows a large number of mobile devices to concurrently upload their local models by exploiting the superposition property of wireless multi-access channels. Due to wireless channel fading, the model aggregation error at the edge server is dominated by the weakest channel among all devices, causing severe straggler issues. In this paper, we propose a relay-assisted cooperative FL scheme to effectively address the straggler issue. In particular, we deploy multiple half-duplex relays to cooperatively assist the devices in uploading the local model updates to the edge server. The nature of the over-the-air computation poses system objectives and constraints that are distinct from those in traditional relay communication systems. Moreover, the strong coupling between the design variables renders the optimization of such a system challenging. To tackle the issue, we propose an alternating-optimization-based algorithm to optimize the transceiver and relay operation with low complexity. Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large. The analysis provides critical insights on relay deployment in the implementation of cooperative FL. Extensive numerical results show that our design achieves faster convergence compared with state-of-the-art schemes.

中文翻译：

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中继辅助合作联邦学习

联邦学习 (FL) 最近已成为一种很有前途的技术，可在网络边缘实现人工智能 (AI)，其中分布式移动设备在边缘服务器的协调下协作训练共享的 AI 模型。为了显着提高 FL 的通信效率，空中计算允许大量移动设备通过利用无线多址信道的叠加特性同时上传其本地模型。由于无线信道衰落，边缘服务器的模型聚合误差由所有设备中最弱的信道支配，导致严重的落后问题。在本文中，我们提出了一种中继辅助合作 FL 方案来有效解决落后者问题。尤其是，我们部署了多个半双工中继，以协同协助设备将本地模型更新上传到边缘服务器。空中计算的性质提出了不同于传统中继通信系统的系统目标和约束。此外，设计变量之间的强耦合使得这种系统的优化具有挑战性。为了解决这个问题，我们提出了一种基于交替优化的算法，以低复杂度优化收发器和中继操作。然后，我们分析了单中继情况下的模型聚合误差，表明如果中继发射功率和中继信道增益足够大，我们的中继辅助方案比没有中继的方案实现了更小的误差。该分析为合作 FL 实施中的中继部署提供了重要见解。广泛的数值结果表明，与最先进的方案相比，我们的设计实现了更快的收敛。