mlsanityData to 14 Oct 2026

Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles

arXiv 2506.02972 ↗First posted 3 Jun 2025cs.LG cs.NI cs.SYcited by 0

Not on the map. Why?

Abstract (arXiv)

Privacy-preserving distributed machine learning (ML) and aerial connected vehicle (ACV)-assisted edge computing have drawn significant attention lately. Since the onboard sensors of ACVs can capture new data as they move along their trajectories, the continual arrival of such 'newly' sensed data leads to online learning and demands carefully crafting the trajectories. Besides, as typical ACVs are inherently resource-constrained, computation- and communication-efficient ML solutions are needed. Therefore, we propose a computation- and communication-efficient online aerial federated learning (2CEOAFL) algorithm to take the benefits of continual sensed data and limited onboard resources of the ACVs. In particular, considering independently owned ACVs act as selfish data collectors, we first model their trajectories according to their respective time-varying data distributions. We then propose a 2CEOAFL algorithm that allows the flying ACVs to (a) prune the received dense ML model to make it shallow, (b) train the pruned model, and (c) probabilistically quantize and offload their trained accumulated gradients to the central server (CS). Our extensive simulation results show that the proposed 2CEOAFL algorithm delivers comparable performances to its non-pruned and nonquantized, hence, computation- and communication-inefficient counterparts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 3 Jun 2025.

Builds on

16 of 26 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,262
  3. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  4. 2018Sparsified SGD with Memorycited by 376
  5. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  6. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
  7. 2020Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarmscited by 31
  8. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  9. 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
  10. 2022Federated Learning via Unmanned Aerial Vehiclecited by 11
  11. 2023Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneitycited by 10
  12. 2024Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networkscited by 4
4 more
  1. 2024Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocationcited by 3
  2. 2024Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systemscited by 1
  3. 2024Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrivalcited by 1
  4. 2025Split Federated Learning for Low-Altitude Wireless Networks: Joint Sensing, Communication, Computation, and Control Co-designcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing
  2. 2026Client-Verifiable and Efficient Federated Unlearning in Low-Altitude Wireless Networks
  3. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
  4. 2026Learn for Variation: Efficient AAV Trajectory Learning through a Differentiable Wireless World Model
  5. 2026Quantization Impact on the Accuracy and Communication Efficiency Trade-off in Federated Learning for Aerospace Predictive Maintenance
  6. 2026Spatiotemporal Continual Federated Learning for Agentic Multi-UAV Edge Networks: Mitigating Catastrophic Forgetting
  7. 2024RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility
  8. 2025Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach