Domain-Aware Hierarchical Federated Learning for UAV Swarms Across Heterogeneous Environments
Paper recorded by Signals 4 on 2026-11-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-11-01 on arXiv · recorded by Signals 4 on 2026-11-03
Category: cs.LG · 机器学习 · first seen 2026-11-03
Abstract
Hierarchical federated learning (HFL) enables communication-efficient training in unmanned aerial vehicle (UAV) swarms, but performance degrades under changing and heterogeneous environments. This paper proposes domain-aware HFL (DA-HFL), integrating mobility-informed clustering, cluster-level aggregation, cloud-based Fisher-weighted merging, a domain-adversarial shared backbone, environment-speci
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Cite this page: Domain-Aware Hierarchical Federated Learning for UAV Swarms Across Heterogeneous Environments: the #46 most recent of 703 cs.LG papers we have recorded (as of 2026-11-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/domain-aware-hierarchical-federated-learning-for-uav-swarms-across-heterogeneous.html
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