FLSTRA: Federated Learning in Stratosphere

• Published in: IEEE transactions on wireless communications Vol. 23; no. 2; pp. 1052 - 1067
• Main Authors: Farajzadeh, Amin, Yadav, Animesh, Abbasi, Omid, Jaafar, Wael, Yanikomeroglu, Halim
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Clients; Computational modeling; Convergence; Data models; Delay; delay minimization; Delays; Federated learning; High altitude; High altitude platform station; Iterative algorithms; Iterative methods; Line of sight; Machine learning; Resource allocation; Simultaneous wireless information and power transfer; Stratosphere; Training; Upper bounds; Wearable computers; Wireless communication
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2023.3285435

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.