HFSL-CUNs: A Hierarchical Federated Split Learning Framework for Cluster-Based and UAV-Assisted Edge-Fog-Cloud Networks

Abstract

The increasing deployment of Internet of Things (IoT) applications has created a growing need for distributed learning frameworks that can operate efficiently across resource-constrained edge environments while preserving data privacy. Federated Learning (FL) enables collaborative model training without sharing raw data. However, its communication overhead, computational burden, and limited scalability make it less suitable for large-scale hierarchical edge networks. In this paper, we propose Hierarchical Federated Split Learning (HFSL), a unified (UAV)-assisted edge-fog-cloud framework that combines the complementary strengths of FL and Split Learning (SL) to improve communication efficiency, scalability, and privacy. HFSL introduces activation similarity-based clustering and spatio-temporal activation filtering to reduce redundant communication, adaptive UAV altitude optimization to improve wireless connectivity under dynamic network conditions, and a privacy-preserving training strategy based on Differential Privacy (DP) and activation-level leakage mitigation. Extensive experiments across eight image and tabular benchmark datasets demonstrate that HFSL outperforms state-of-the-art FL and SL methods on the more challenging heterogeneous benchmarks, reducing energy consumption by up to 50%, training latency by 40%, and communication overhead by 35%, while improving classification accuracy by up to 6%. These results show that HFSL provides an effective and scalable distributed learning framework for next-generation UAV-assisted edge networks.