One of the most critical challenges for deploying distributed learning solutions, such as federated learning (FL), in wireless networks is the limited battery capacity of mobile clients. While it is a common belief that the major energy consumption of mobile clients comes from the uplink data transmission, this paper presents a novel finding, namely the channel decoding operation also contributes significantly to the overall energy consumption of mobile clients in FL. Motivated by this new observation, we propose an energy-efficient adaptive channel decoding scheme that leverages the intrinsic robustness of FL to model errors. In particular, the robustness is exploited to reduce the energy consumption of channel decoders at mobile clients by adaptively adjusting the number of decoding iterations. We theoretically prove that wireless FL with communication errors can converge at the same rate as the case with error-free communication as long as the bit error rate (BER) is properly constrained. An adaptive channel decoding scheme is then proposed to improve the energy efficiency of wireless FL systems. Experimental results demonstrate that the proposed method maintains the same learning accuracy while reducing the channel decoding energy consumption by 20% when compared to existing approaches.

翻译：在无线网络中部署分布式学习解决方案（如联邦学习（FL））面临的最关键挑战之一是移动客户端的有限电池容量。尽管普遍认为移动客户端的主要能耗来自上行链路数据传输，但本文提出了一项新颖的发现，即信道解码操作同样显著地贡献了FL中移动客户端的整体能耗。基于这一新观察，我们提出了一种节能的自适应信道解码方案，该方案利用了FL对模型误差的固有鲁棒性。具体而言，通过自适应调整解码迭代次数来利用这种鲁棒性，以降低移动客户端信道解码器的能耗。我们从理论上证明，只要误码率（BER）得到适当约束，存在通信误差的无线FL能够以与无误差通信情况相同的速率收敛。随后提出了一种自适应信道解码方案，以提高无线FL系统的能效。实验结果表明，与现有方法相比，所提方法在保持相同学习精度的同时，将信道解码能耗降低了20%。