# FLSTRA: Federated Learning in Stratosphere

arXiv: https://arxiv.org/abs/2302.00163 · HTML: https://mlsanity.com/paper/2302.00163/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning](https://mlsanity.com/paper/2002.11343/) (2020)
- [Federated Learning for Internet of Things: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2111.07494/) (2021)
- [FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs](https://mlsanity.com/paper/2205.07216/) (2022)
- [A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data](https://mlsanity.com/paper/1511.02746/) (2015)
- [A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future](https://mlsanity.com/paper/2007.15088/) (2020)
- [A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles](https://mlsanity.com/paper/1801.01656/) (2018)
- [Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality](https://mlsanity.com/paper/2211.08705/) (2022)
- [Caching and Computation Offloading in High Altitude Platform Station (HAPS) Assisted Intelligent Transportation Systems](https://mlsanity.com/paper/2106.14928/) (2021)
- [Self-Evolving Integrated Vertical Heterogeneous Networks](https://mlsanity.com/paper/2106.13950/) (2021)
- [Joint User Association and Beamforming in Integrated Satellite-HAPS-Ground Networks](https://mlsanity.com/paper/2204.13257/) (2022)
- [Joint User Association and Resource Allocation in the Uplink of Heterogeneous Networks](https://mlsanity.com/paper/1911.12697/) (2019)

## Built on by (most cited first, 1 of 1)

- [Tackling Non-IIDness in HAPS-Aided Federated Learning](https://mlsanity.com/paper/2401.05308/) (2024)

## Abstract (arXiv)

We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithm to achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problem is non-convex; thus, we propose an iterative algorithm to solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.