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:this https URL

在本文中，我们提出了利用本地参数共享的异构联邦学习（FedLPS）来解决边缘计算环境中多任务部署和资源消耗不均匀问题，并通过模型剪枝算法和异构模型聚合算法来减少计算资源的消耗。实验证明，FedLPS 相对于现有联邦学习框架在性能上有很大的提升，可以将计算资源的消耗降低 21.3%。