FedEdge-Adapt: Adaptive Federated Learning for Heterogeneous Edge AI Systems

Abstract

Federated learning (FL) on heterogeneous edge networks faces a fundamental tension: standard aggregation protocols assume client homogeneity, yet real-world edge deployments span device tiers with $7 \times$ compute and $\mathbf{1 0} \times$ bandwidth disparities. Slow clients become stragglers that stall synchronous rounds, while uniform gradient compression degrades accuracy on bandwidth-constrained devices. This paper presents FedEdge-Adapt, a novel adaptive federated learning framework that jointly addresses device heterogeneity, straggler mitigation, and communication efficiency without sacrificing model quality. FedEdge-Adapt introduces three tightly coupled mechanisms: (1) tier-aware gradient compression that applies device-class-specific sparsification ratios, (2) drift-corrected aggregation that reweights client updates based on staleness and data heterogeneity, and (3) predictive client selection that anticipates dropout-prone devices using a lightweight resource oracle. We evaluate FedEdge-Adapt on a 30-node heterogeneous edge network over 150 communication rounds using the CIFAR-10 dataset under non-IID distributions ($\alpha=0.5$ Dirichlet) and compare against FedAvg, FedProx, and SCAFFOLD baselines. FedEdge-Adapt achieves 85.44% global accuracy, a 6.85 percentage-point improvement over FedAvg, while simultaneously reducing round latency by $\mathbf{6 7. 4 \%}$, communication overhead by $\mathbf{3 4. 0 \%}$, and client dropout rate by $\mathbf{5 4. 0 \%}$. Convergence is reached in 18 rounds versus 31 for FedAvg. Extended experiments across 100+ rounds confirm long-term stability with no late-stage divergence.