Lightweight Federated Learning in Mobile Edge Computing with Statistical and Device Heterogeneity Awareness

TL;DR

This paper introduces a personalized federated learning framework that effectively reduces communication and computation costs in heterogeneous mobile edge environments by combining model decoupling, gradient sparsification, and pruning.

Contribution

It presents a novel parameter decoupling approach enabling targeted compression and personalization, along with a theoretical convergence analysis and resource-aware optimization for efficient training.

Findings

Faster convergence compared to traditional FL methods.

Significant reduction in communication and computation costs.

Negligible accuracy loss in heterogeneous environments.

Abstract

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.