In the expanding realm of machine learning (ML) within edge computing, the efficient exchange of information in federated learning (FL) environments is paramount. FL's decentralized nature often leads to significant communication bottlenecks, particularly in settings where resources are limited. Traditional data compression techniques, such as quantization and pruning, provide partial solutions but can compromise model performance or necessitate costly retraining. Our paper addresses this issue through \textit{FedSZ}, a novel lossy compression-based FL framework. \textit{FedSZ} is designed to minimize the size of local model updates without impacting model performance. Our framework features a compression pipeline integrating data partitioning, lossy and lossless model parameters, metadata compression, and efficient serialization. We conduct a thorough evaluation of \textit{FedSZ} utilizing a variety of lossy compressors, among which SZ2 emerged as the most effective, consistently performing well across diverse neural network architectures, including AlexNet, MobileNetV2, and ResNet50, and datasets such as CIFAR-10, Caltech101, and FMNIST. A relative error bound of 1E-2 balances compression and data integrity, achieving compression ratios ranging from $5.55\mbox{--}12.61\times$. Furthermore, we observed that the runtime overhead introduced by \textit{FedSZ} is minimal, at less than $4.7\%$, compared to a significant reduction in network transfer times, which we noted to exceed $13.3\times$ reduction or saving of over $100$s in edge networks operating at 10Mbps. Our findings firmly establish the efficacy of \textit{FedSZ}, offering valuable insights for achieving an optimal balance between communication efficiency and model performance in FL settings, particularly in edge computing environments.

翻译：在边缘计算中机器学习的扩展领域，联邦学习环境下的信息高效交换至关重要。联邦学习的去中心化特性常导致显著通信瓶颈，尤其在资源受限场景中。传统数据压缩技术（如量化和剪枝）虽提供部分解决方案，但可能损害模型性能或需高昂重训练成本。本文通过提出新型有损压缩联邦学习框架\textit{FedSZ}解决这一问题，该框架旨在最小化局部模型更新规模而不影响模型性能。我们的框架包含集成数据分片、有损与无损模型参数压缩、元数据压缩及高效序列化的压缩流水线。通过多种有损压缩器对\textit{FedSZ}进行全面评估，其中SZ2表现最优，在AlexNet、MobileNetV2、ResNet50等多样化神经网络架构及CIFAR-10、Caltech101、FMNIST等数据集上持续表现良好。采用相对误差界1E-2平衡压缩与数据完整性，可实现压缩比$5.55\mbox{--}12.61\times$。此外，我们观察到\textit{FedSZ}引入的运行时开销极小（低于$4.7\%$），而网络传输时间显著减少——在10Mbps边缘网络中传输时间降低超过$13.3\times$或节省$100$秒以上。研究结果充分验证了\textit{FedSZ}的有效性，为在联邦学习环境（尤其是边缘计算场景）中实现通信效率与模型性能的最优平衡提供了重要见解。