用个性化编码器和低秩适配解码器，大幅降低大规模MIMO的信道反馈通信开销。
Fed-PELAD: Communication-Efficient Federated Learning for Massive MIMO CSI Feedback with Personalized Encoders and a LoRA-Adapted Shared Decoder
- 用户端用私有编码器学设备特性，基站用低秩适配共享解码器减少传输量。
- 相比传统方法通信成本降42.97%，异构环境下信道反馈准确率提升1.2 dB。
- 适合大规模无线系统中需低延迟、高隐私保护的场景，如5G/6G网络部署。
本文针对大规模MIMO系统中深度学习进行信道状态信息（CSI）反馈时面临的通信开销大、数据异构性强及隐私保护难等关键问题，提出一种新型联邦学习框架Fed-PELAD，融合个性化编码器与基于低秩适配（LoRA）的共享解码器。具体而言，各用户设备（UE）本地训练个性化编码器以捕捉设备特有的信道特征，而由基站（BS）协调更新的共享解码器通过LoRA实现参数高效更新。该设计仅需传输紧凑的LoRA适配器参数而非完整模型，显著降低通信负担。为进一步提升收敛稳定性，引入交替冻结策略与校准的学习率比。在3GPP标准信道模型上的大量仿真表明，相较于传统方法，Fed-PELAD在异构条件下将上行通信成本降低42.97%，同时实现1.2 dB的信道反馈精度增益。
原文摘要 · Abstract (English)
This paper addresses the critical challenges of communication overhead, data heterogeneity, and privacy in deep learning for channel state information (CSI) feedback in massive MIMO systems. To this end, we propose Fed-PELAD, a novel federated learning framework that incorporates personalized encoders and a LoRA-adapted shared decoder. Specifically, personalized encoders are trained locally on each user equipment (UE) to capture device-specific channel characteristics, while a shared decoder is updated globally via the coordination of the base station (BS) by using Low-Rank Adaptation (LoRA). This design ensures that only compact LoRA adapter parameters instead of full model updates are transmitted for aggregation. To further enhance convergence stability, we introduce an alternating freezing strategy with calibrated learning-rate ratio during LoRA aggregation. Extensive simulations on 3GPP-standard channel models demonstrate that Fed-PELAD requires only 42.97\% of the uplink communication cost compared to conventional methods while achieving a performance gain of 1.2 dB in CSI feedback accuracy under heterogeneous conditions.
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