In Low Earth Orbit (LEO) mega constellations, there are relevant use cases, such as inference based on satellite imaging, in which a large number of satellites collaboratively train a machine learning model without sharing their local datasets. To address this problem, we propose a new set of algorithms based on Federated learning (FL), including a novel asynchronous FL procedure based on FedAvg that exhibits better robustness against heterogeneous scenarios than the state-ofthe-art. Extensive numerical evaluations based on MNIST and CIFAR-10 datasets highlight the fast convergence speed and excellent asymptotic test accuracy of the proposed method.

Salicylic acid (SA) plays an important role in the regulation of plant growth and development in response to water deficit. The effect of SA (0, 0.4 and 0.8 mM) on some physiological parameters of three soybean genotypes was investigated in three irrigation schedules included (85%, 65% and 45% of field capacity) during 2014-2015. Results showed that water deficit decreased stomatal conductance, leaf area index, relative water content, membrane stability index, yield components and grain yield particularly in L17 genotype. Activities of superoxide dismutase, ascorbate peroxidase and concentration of hydrogen peroxide, proline and total protein were increased in response to water deficit as well as SA applications. SA inhibited catalase activity resulting in increased hydrogen peroxide accumulation in soybean genotypes. Application of 0.4 mM SA decreased the adverse effects of water deficit in soybean genotypes by elevation of antioxidant enzymes activity and reducing malondialdehyde formation especially in Williams genotype.

The emergence of mega-constellations of interconnected satellites has a major impact on the integration of cellular wireless and non-terrestrial networks (NTN). This paper studies the problem of running a federated learning (FL) algorithm within a low Earth orbit (LEO) constellation of satellites connected with intra-orbit inter-satellite links (ISL). Satellites apply on-board machine learning and transmit the local parameters to the parameter server (PS). The main contribution is a novel approach to enhance FL in satellite constellations using intra-orbit ISLs. The key idea is to rely on the predictability of satellite visits to create a system design in which ISLs mitigate the impact of intermittent connectivity and transmit the aggregated parameters to the PS. We first devise a synchronous FL, which is then extended towards an asynchronous FL for the case of sparse satellite visits to the PS. An efficient use of the satellite resources is attained by sparsification-based compression the aggregated parameters of each orbit before forwarding to the PS. Performance is evaluated in terms of accuracy and the required size of data to be transmitted. The numerical results indicate a faster convergence rate of the presented approach compared with the state-of-the-art FL on satellite constellations.

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Distributed training of machine learning models directly on satellites in low Earth orbit (LEO) is considered. Based on a federated learning (FL) algorithm specifically targeted at the unique challenges of the satellite scenario, we design a scheduler that exploits the predictability of visiting times between ground stations (GS) and satellites to reduce model staleness. Numerical experiments show that this can improve the convergence speed by a factor three.