HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning (2002.11343v2)

Abstract: Federated Learning (FL) has been proposed as an appealing approach to handle data privacy issue of mobile devices compared to conventional machine learning at the remote cloud with raw user data uploading. By leveraging edge servers as intermediaries to perform partial model aggregation in proximity and relieve core network transmission overhead, it enables great potentials in low-latency and energy-efficient FL. Hence we introduce a novel Hierarchical Federated Edge Learning (HFEL) framework in which model aggregation is partially migrated to edge servers from the cloud. We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization. To solve the problem, we propose an efficient resource scheduling algorithm in the HFEL framework. It can be decomposed into two subproblems: \emph{resource allocation} given a scheduled set of devices for each edge server and \emph{edge association} of device users across all the edge servers. With the optimal policy of the convex resource allocation subproblem for a set of devices under a single edge server, an efficient edge association strategy can be achieved through iterative global cost reduction adjustment process, which is shown to converge to a stable system point. Extensive performance evaluations demonstrate that our HFEL framework outperforms the proposed benchmarks in global cost saving and achieves better training performance compared to conventional federated learning.

An Overview of HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning

The paper "HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning" offers an insightful approach to addressing the challenges associated with Federated Learning (FL) in mobile environments. It introduces the Hierarchical Federated Edge Learning (HFEL) framework, which strategically employs edge servers to facilitate partial model aggregation, thereby optimizing both communication and computation resources.

Federated Learning and the Role of HFEL

Federated Learning has been recognized as an effective method for ensuring data privacy by avoiding raw data transfer to the cloud. Instead, it aggregates model updates from multiple devices. However, FL faces significant challenges in communication and energy efficiency, especially with the extensive WAN communications involved between devices and a centralized cloud server.

HFEL addresses these challenges by introducing intermediary edge servers positioned closer to the devices, which perform local aggregations of the model updates before forwarding them cloud-ward. This hierarchical aggregation strategy is intended to reduce WAN latency and energy consumption significantly, proving beneficial in scenarios where real-time edge computing becomes essential.

Methodology and Problem Formulation

The authors formulate a dual-layer optimization problem within the HFEL framework that integrates the allocation of computation and communication resources with the dynamic association of devices to edge servers. This optimization strives for a global minimization of costs associated with learning tasks, where costs are broadly defined in terms of energy consumption and operational latency.

To tackle the complex optimization problem, the authors propose a decomposition into two subproblems: resource allocation and edge association. These are addressed through iterative strategies designed to minimize costs effectively and ensure converging to a stable, efficient system throughput, thereby maximizing the use of available resources.

Performance Evaluation

The numerical evaluations depicted in the paper suggest that the HFEL framework outperforms traditional FL models, notably FedAvg, in both cost efficiency and learning performance. The framework achieved substantial reductions in global energy consumption and latency, validated across varying numbers of devices and edge servers, and under different data loading conditions.

The simulations reveal that HFEL provides better convergence performance in terms of test accuracy, training accuracy, and training loss, compared to legacy FL infrastructures. This is attributable to the framework's ability to localize computations and resource allocations partially at the edge layer, demonstrating lower communication overheads and improved model training efficiencies.

Implications and Future Directions

The HFEL framework's hierarchical approach offers promising improvements in edge-oriented federated learning systems by optimizing both resource usage and learning performance. By reducing dependency on centralized cloud computing and introducing flexible model aggregation at the edge, HFEL presents a scalable solution for the AI-driven advancements anticipated within the IoT and mobile computing domains.

Future work could delve into adaptive strategies for dynamically optimizing the resource allocation and edge association in real-time, considering fluctuating network conditions and device mobility. Additionally, exploring privacy-preserving aggregation techniques in the hierarchical setting could strengthen the model's usability across more privacy-sensitive applications.

In conclusion, the HFEL framework marks a significant enhancement over existing federated learning models by tactfully leveraging edge computing potentials to optimize resource allocation and cost efficiency, particularly relevant for contemporary and future AI-powered edge applications.