Federated Learning (FL) has emerged as a promising solution in Edge Computing (EC) environments to process the proliferation of data generated by edge devices. By collaboratively optimizing the global machine learning models on distributed edge devices, FL circumvents the need for transmitting raw data and enhances user privacy. Despite practical successes, FL still confronts significant challenges including constrained edge device resources, multiple tasks deployment, and data heterogeneity. However, existing studies focus on mitigating the FL training costs of each single task whereas neglecting the resource consumption across multiple tasks in heterogeneous FL scenarios. In this paper, we propose Heterogeneous Federated Learning with Local Parameter Sharing (FedLPS) to fill this gap. FedLPS leverages principles from transfer learning to facilitate the deployment of multiple tasks on a single device by dividing the local model into a shareable encoder and task-specific encoders. To further reduce resource consumption, a channel-wise model pruning algorithm that shrinks the footprint of local models while accounting for both data and system heterogeneity is employed in FedLPS. Additionally, a novel heterogeneous model aggregation algorithm is proposed to aggregate the heterogeneous predictors in FedLPS. We implemented the proposed FedLPS on a real FL platform and compared it with state-of-the-art (SOTA) FL frameworks. The experimental results on five popular datasets and two modern DNN models illustrate that the proposed FedLPS significantly outperforms the SOTA FL frameworks by up to 4.88% and reduces the computational resource consumption by 21.3%. Our code is available at:https://github.com/jyzgh/FedLPS.

翻译：联邦学习（FL）已成为边缘计算环境中处理终端设备生成数据激增问题的有效方案。通过分布式边缘设备上协同优化全局机器学习模型，联邦学习规避了传输原始数据的必要性，并增强了用户隐私保护。尽管取得实际成功，联邦学习仍面临终端设备资源受限、多任务部署及数据异构性等重大挑战。然而现有研究侧重缓解单任务联邦学习的训练成本，却忽视了异构联邦学习场景中多任务间的资源消耗问题。本文提出基于局部参数共享的异构联邦学习（FedLPS）以弥补这一空白。FedLPS借鉴迁移学习原理，通过将本地模型划分为可共享编码器与任务专用编码器，实现在单一设备上部署多任务。为进一步降低资源消耗，该框架采用通道级模型剪枝算法，综合考虑数据与系统异构性以缩减本地模型规模。此外，本文提出新型异构模型聚合算法以集成FedLPS中的异构预测器。我们在真实联邦学习平台上实现所提出的FedLPS，并与当前最优联邦学习框架进行对比。基于五个主流数据集及两个现代深度神经网络模型的实验结果表明，FedLPS较最优联邦学习框架性能提升高达4.88%，同时降低21.3%的计算资源消耗。相关代码已开源：https://github.com/jyzgh/FedLPS