FLSTRA: Federated Learning in Stratosphere

• URL: http://arxiv.org/abs/2302.00163v3
• Date: Fri, 9 Jun 2023 14:26:29 GMT
• Title: FLSTRA: Federated Learning in Stratosphere
• Authors: Amin Farajzadeh, Animesh Yadav, Omid Abbasi, Wael Jaafar, Halim Yanikomeroglu
• Abstract summary: A high altitude platform station facilitates a number of terrestrial clients to collaboratively learn a global model without the training data. We develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay. Second, we propose a communication and resource-aware algorithm to achieve the target FL accuracy while deriving an upper bound for its convergence.
• Score: 22.313423693397556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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