Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, FL often suffers from significant communication and computational overhead, limiting its scalability and sustainability. In this work, we introduce a Full Compression Pipeline (FCP) for FL in communication-constrained environments. FCP integrates three complementary deep compression techniques (pruning, quantization, and Huffman encoding) into a unified end-to-end framework. By compressing local models and communication payloads, FCP substantially reduces transmission costs and resource consumption while maintaining competitive accuracy. To quantify its impact, we develop an evaluation framework that captures both communication and computation overheads as a unified model cost, allowing a holistic assessment of efficiency trade-offs. The pipeline is evaluated in an independent and identically distributed (IID) and non-IID data setting. In one representative scenario, training a ResNet-12 model on the CIFAR-10 dataset with ten clients and a 2 Mbps bandwidth, the FCP achieves more than 11$\times$ reduction in model size, with only a 2% drop in accuracy compared to the uncompressed baseline. This results in an FL training that is more than 60% faster.

翻译：联邦学习（Federated Learning, FL）通过在分布式客户端间协同训练模型而不共享原始数据，从而保护隐私。然而，联邦学习通常面临显著通信与计算开销，限制其可扩展性与可持续性。本文针对通信受限场景提出一种全压缩管线（Full Compression Pipeline, FCP），该管线将三种互补的深度压缩技术（剪枝、量化和霍夫曼编码）整合至统一端到端框架中。通过压缩本地模型与通信负载，FCP在保持竞争性精度的同时显著降低传输成本与资源消耗。为量化其影响，我们构建了一个评估框架，将通信与计算开销统一为模型成本，从而实现对效率权衡的整体评估。该管线在独立同分布（IID）与非独立同分布（non-IID）数据场景下进行了评估。在一个典型场景中（基于CIFAR-10数据集、十位客户端、2Mbps带宽训练ResNet-12模型），FCP实现模型尺寸缩减超过11倍，且相较于未压缩基线仅降低2%的准确率。这使得联邦训练速度提升超过60%。