Federated learning (FL) can be implemented in large-scale wireless networks in a hierarchical way, introducing edge servers as relays between the cloud server and devices. These devices are dispersed within multiple clusters coordinated by edges. However, the devices are typically mobile users with unpredictable trajectories, and the impact of their mobility on the model training process is not well-studied. In this work, we propose a newMobIlity-Driven feDeratedLEarning framework, namely MIDDLE. MIDDLE addresses unbalanced model updates by capitalizing on model aggregation opportunities on mobile devices due to their mobility across edges. It consists of two components: on-device model aggregation, which aggregates models from different edges carried by mobile devices as they move across edges, and in-edge device selection, adjusting the current edge optimization direction through careful device selection. Theoretical analysis emphasizes that on-device model aggregation can reduce bias in model updating on edges and the cloud, thereby accelerating the FL model convergence. Building on this analysis, we introduce on-device global control averaging, modifying the training process on mobile devices and extending MIDDLE into$\text{MIDDLE}^{+}$. Extensive experimental results validate that MIDDLE and$\text{MIDDLE}^{+}$can reduce the time steps to reach the target accuracy by 19.44% and 20.37% at least, respectively.

Hierarchical Federated Learning (HFL) is a practical implementation of federated learning in mobile edge computing, employing edge servers as intermediaries between mobile devices and the cloud server for device coordination and cloud communication. However, the devices are usually mobile users with unpredictable mobile trajectories and statistical heterogeneity, leading to the edge models optimized along dynamic edge data distribution directions and further resulting in instability and slow convergence of the global model. In this work, we propose a Mobility-Aware deviCe sampling algorithm in HFL, namely MACH, which can dynamically maintain the device sampling strategy at each edge to accelerate the convergence of the global model. First, we analyze the convergence bound of HFL with mobile devices under arbitrary device sampling probabilities. Based on this convergence bound, we formalize the sampling optimization problem for mobility-aware device sampling, aiming to minimize the convergence error under time-averaged cost constraints, while taking the limited device-edge wireless channel capacity into account. Next, we introduce the MACH algorithm, consisting of two underlying components: experience updating and edge sampling. Experience updating utilizes an upper confidence bound method to estimate device statistical information online, and edge sampling customizes a sampling strategy on each edge based on the estimated device statistical information. Finally, extensive experimental results through real-world mobile device trajectories validate that MACH can reduce the time required to achieve a target accuracy by 25.00% - 56.86%.

Federated learning (FL) in large-scale wireless networks is implemented in a hierarchical way by introducing edge servers as relays between the cloud server and devices, where devices are dispersed within multiple clusters coordinated by edges. However, the devices are usually mobile users with unpredictable mobile trajectories, whose effects on the model training process are still less studied. In this work, we propose a new MobIlity-Driven feDerated LEarning framework, namely MIDDLE in wireless networks, which can relieve unbalanced and biased model updates by leveraging the new model aggregation opportunities on mobile devices due to their mobility across edges. Specifically, mobile devices can have different models while traversing across edges, and adequately aggregate these models on the device. By theoretical analysis, we can show that this on-device model aggregation can reduce the bias of model updating on edges and cloud, and then accelerate the convergence of model training in FL. Then, we define a model similarity utility to measure the difference in gradient updates among various models, which guides the adaptive on-device model aggregation and in-edge device selection to facilitate the comprehensive information sharing between edges. Extensive experiment results validate that MIDDLE can achieve 1.51 × −6.85 × speedup on the model training, compared with the state-of-the-art model training approaches in hierarchical FL.

In Mobile Edge Computing (MEC), latency-sensitive mobile applications comprising dependent tasks can be scheduled to edge or cloud servers to reduce latency and execution costs. However, existing algorithms based on deadline distribution can hardly satisfy tight application deadlines in heterogeneous MEC due to lacking a global view of the future impacts on descendant tasks. To fill in this gap, we formulate the deadline-constrained cost optimization problem for dependent task scheduling in MEC and propose a low-complexity scheduling algorithm that considers a single task's future impacts in two stages. Specifically: (1) In the edge scheduling stage, each task is scheduled according to its successors’ latest start times instead of its sub-deadline to alleviate the lateness of its successors. An edge-only schedule plan is generated by scheduling tasks only on edge servers to save execution costs. (2) In the cloud offloading stage, in order to utilize the powerful cloud resources to satisfy the deadline, the edge-only schedule plan missing the deadline is efficiently modified by properly offloading multiple successive tasks to the cloud. Simulation results show the substantial advantage of the proposed algorithm over baselines in both online and offline scenarios.