Federated learning enables collaborative machine learning while preserving data privacy, but high communication and computation costs, exacerbated by statistical and device heterogeneity, limit its practicality in mobile edge computing. Existing compression methods like sparsification and pruning reduce per-round costs but may increase training rounds and thus the total training cost, especially under heterogeneous environments. We propose a lightweight personalized FL framework built on parameter decoupling, which separates the model into shared and private subspaces, enabling us to uniquely apply gradient sparsification to the shared component and model pruning to the private one. This structural separation confines communication compression to global knowledge exchange and computation reduction to local personalization, protecting personalization quality while adapting to heterogeneous client resources. We theoretically analyze convergence under the combined effects of sparsification and pruning, revealing a sparsity-pruning trade-off that links to the iteration complexity. Guided by this analysis, we formulate a joint optimization that selects per-client sparsity and pruning rates and wireless bandwidth to reduce end-to-end training time. Simulation results demonstrate faster convergence and substantial reductions in overall communication and computation costs with negligible accuracy loss, validating the benefits of coordinated and resource-aware personalization in resource-constrained heterogeneous environments.

Federated learning (FL) provides a decentralized framework that enables universal model training through collaborative efforts on mobile nodes, such as smart vehicles in the Internet of Vehicles (IoV). Each smart vehicle acts as a mobile client, contributing to the process without uploading local data. This method leverages nonindependent and identically distributed (non-IID) training data from different vehicles, influenced by various driving patterns and environmental conditions, which can significantly impact model convergence and accuracy. Although client selection can be a feasible solution for non-IID issues, it faces challenges related to selection metrics. Traditional metrics evaluate client data quality independently per round and require client selection after all clients complete local training, leading to resource wastage from unused training results. In the IoV context, where vehicles have limited connectivity and computational resources, information asymmetry in client selection risks clients submitting false information, potentially making the selection ineffective. To tackle these challenges, we propose a novel long-term client-selection federated learning based on truthful auction (LCSFLA). This scheme maximizes social welfare with consideration of long-term data quality using a new assessment mechanism and energy costs, and the advised auction mechanism with a deposit requirement incentivizes client participation and ensures information truthfulness. We theoretically prove the incentive compatibility and individual rationality of the advised incentive mechanism. Experimental results on various datasets,including those from IoV scenarios, demonstrate its effectiveness in mitigating performance degradation caused by non-IID data.

In response to the escalating data volumes and the pressing need for reduced network latency in Mobile Edge Computing (MEC), this paper delves into the sphere of edge computing. Many MEC platforms are turning to container-based virtualization and leveraging image layering to cut down transmission costs. Amid this shift, applications are growing more complex, composed of multiple tasks and demanding diverse execution environments. However, existing research has mainly concentrated on task scheduling within edge systems, sidelining the vital aspect of preparing runtime environments on MEC servers. To bridge this gap, our paper tackles task scheduling complexities, including data and layer dependencies. It offers an integrated solution that optimizes task scheduling and layer loading across MEC servers, all with the goal of minimizing the total computation completion time. To address this NP-hard problem, we present a heuristic task-scheduling algorithm rooted in the genetic algorithm (GA) and complement it with an in-depth exploration of layer-loading policies. Our experiments conclusively demonstrate the substantial reduction in total computation completion time achievable through this approach.

Deployed with computation resources, fog radio access network (F-RAN) provides a promising solution for computation offloading. To take full advantage of two-tier computing in the F-RAN, on one hand, it is inevitable to design, between edge and cloud, an efficient and flexible fronthaul transmission strategy, and fronthaul resource allocation should be jointly optimized with allocation of tasks and other resources. On the other hand, a robust computation provisioning strategy that can avoid failures caused by estimation errors of available computation resources is necessary. In this work, considering the fronthaul compression and the uncertain computation capacity, we design an energy-efficient computation offloading mechanism in the F-RAN. The formulated problem is challenging to solve due to coupled communication and computation resource constraints and binary variables for task placement. We show that the problem can be recast as a convex problem if binary variables are relaxed. On top of this result, we propose an efficient algorithm to find a stationary solution. Through simulation, we demonstrate that the proposed algorithm outperforms the baseline algorithm significantly and converges to the near-optimal point solution. Besides, we compare the F-RAN with single-tier computing systems and show the excellence of the F-RAN in energy conservation for mobile devices.

In this paper, we investigate robust energy efficiency design for multicast downlink cloud radio access network (C- RAN) for both data-sharing and compression strategies. The two strategies mainly differ in whether the baseband units pool compresses the user messages before transmitting them to remote radio heads (RRH). The performance of worst-case energy efficiency for both strategies is compared by formulating the robust energy efficiency maximization problem subjected to finite RRH power budgets, limited fronthaul link capacity, and minimum quality-of-service constraints when only imperfect channel state information (CSI) is available at the transmitter. To solve these problems, we cast them into semidefinite program problems and solve them iteratively. Simulation results demon- strate effectiveness of our proposed algorithms and two strategies are compared under imperfect CSI.