While service-oriented low Earth orbit (LEO) satellite edge computing (LSEC) frameworks enable diverse edge services for user tasks, the heterogeneous distribution of terrestrial users causes substantial imbalance in computational task loads across satellites. This asymmetric workload leads to inefficient resource utilization and degraded edge computing performance. To address these challenges, we propose a joint optimization framework that integrates edge service deployment and task offloading, supported by service popularity analysis and spatiotemporal user-task modeling. The framework employs a two-timescale design: at the large timescale, an improved atomic orbital search (iAOS) heuristic dynamically optimizes service placement, configuration, and resource allocation; at the small timescale, a direction-selective multi-agent double deep Q-network (DS-MDDQN) leverages deep reinforcement learning to route tasks to the most suitable processing nodes. Extensive simulations show that our approach significantly outperforms six representative baselines in both user-perceived performance and system-level efficiency. Replacement studies further verify the effectiveness of each component: iAOS enhances resource utilization and reduces task failure through optimized service deployment, while DS-MDDQN mitigates network dynamics and lowers task completion latency via adaptive task offloading.

Remote sensing (RS) applications increasingly operate over distributed infrastructures that integrate space-airground- sea resources with edge intelligence, yet remains challenging to centralize due to geographic dispersion, cross-institution barriers and privacy regulations. Federated learning (FL), a promising privacy-preserving distributed learning paradigm, has garnered wide attention. However, the practical application of FL for RS encounters the issue of label noise stemming from inevitable annotation errors. In this work, we pioneer an early investigation of label noise in distributed RS tasks. We introduce the Federated Representation Optimal Matching (FedROM) framework, which guides robust representation alignment in the presence of noisy labels without requiring auxiliary data or transmitting extra sensitive information. Specifically, FedROM focuses on the robust local updating process, where clients first identify underlying noisy samples from the perspectives of both per-sample loss value and latent representation space. Subsequently, inspired by the optimal transport technique, we adaptively align the latent representations of identified noisy samples with their corresponding closest class centroids with the least representation matching distance, where class centroids are averaged by the latent representations of other relatively clean samples. This reduces the misleading effects caused by noisy samples and guides the model to capture more robust semantic features in the latent representation space. Theoretical analysis proves the robustness and convergence of FedROM. Extensive experiments on two real-world distributed RS datasets covering multi-source domains and varying label noise rates demonstrate the robustness of FedROM against eighteen baseline methods. Meanwhile, FedROM also surpasses its counterparts in conditions of no label noise, narrowing the gap with the centralized training. To facilitate related communities, our code is open-sourced athttps://github.com/Sprinter1999/ROM.

Video prediction is an important yet challenging task that generates future frames based on previous observations. Despite recent progress, existing methods still suffer from motion blur, due to weak motion perception capabilities leading to uncertainty in motion direction. To address this, we propose a Motion Direction Awareness (MDA) mechanism inspired by the direction-selective mechanism in animal visual systems. Specifically, MDA can decompose complex motions into horizontal and vertical components, allowing dimension reduction and independent processing, thereby effectively enhancing motion perception and reducing uncertainty in predicted motion directions. Based on MDA, we design a multi-scale feature fusion network named MDANet for video prediction, which incorporates different scales of spatially encoded features in conjunction with MDA mechanism to extract the temporal evolution information of global and local spatial features. Extensive experiments on representative datasets demonstrate that MDANet can alleviate motion blurring, improving prediction accuracy and temporal consistency over state-of-the-art models. Furthermore, we validate the generalizability and effectiveness of our MDA mechanism by integrating it into other advanced models. The code is available at supplementary.

Routing optimization within a low Earth orbit (LEO) satellite broadband network (LSBN) has seen advancements through deep reinforcement learning (DRL) approaches in academia. Nonetheless, a crucial aspect often overlooked in these approaches pertains to the inference time of deep neural network (DNN) models during the routing of packets. Our investigation reveals that this oversight can significantly impair routing throughput in LSBN. In response, this paper innovatively proposes a decentralized flow-centric DRL approach, shifting the focus from routing individual packets to entire traffic flows. To align with the large-scale feature of LSBN, we embrace a fully-distributed architecture for flow-centric routing, which is modeled as a partially observable Markov decision process. In this construct, each satellite operates as an independent agent, locally classifying flows following a tailor-designed definition, and is responsible for forwarding a flow to an adjacent satellite based on its internal policy. Notably, the DNN inference is conducted only once on each agent to determine the route for the initial packet of a specific flow; subsequent packets are directed along the same route. Recognizing the potential impact of dynamic LSBN topologies on routing performance, we also introduce an adaptive flow routing update scheme. This scheme is completely free from LSBN environment modelling and aims to bolster the efficacy of the flow-centric approach. Comparative experiments showcase the superiority of the proposed approach over baseline algorithms across various metrics. Consequently, the flow-centric DRL approach can enable high-throughput traffic transmission for LSBN.

Video prediction is a challenging spatiotemporal prediction task that generates future frames based on historical observations. Although recently proposed deep learning-based methods significantly outperform legacy approaches, there still exist gaps between prediction and ground truth, primarily rooted in edge and motion blurring. On the one hand, since conventional performance metrics like Mean Square Error (MSE) and Structure Similarity Index Measure (SSIM) cannot decently evaluate this deficiency, we design a 3D Frequency Loss (3DFL) metric to better assess the similarity of predicted video frames. On the other hand, edge and motion blurring is mainly attributed to the predictive model's insufficient attention to high spatial frequency arising from rapid pixel value variations at object edges, and it is observed that shallow networks are more adept at capturing high spatial frequency information. Therefore, aiming to alleviate edge and motion blurring, we propose a novel video prediction model termed SDFNet that can extract and integrate both spatially encoded shallow and deep-level features. To accommodate SDFNet's multi-branch input structure, a frequency adaptive translator (FATranslator) is derived, which leverages involution operators to adaptively extract inter-frame temporal dependencies from different spatial encoding layers, and further mitigates motion blurring. Extensive experiments demonstrate that our proposed model achieves significant improvements in prediction accuracy and temporal consistency over the current state-of-the-art models on various benchmarks. The results highlight the importance of spatial frequency modeling for enhancing video prediction performance, contributing to the advancement of multimedia technologies.

Legacy computing offloading approaches are originally designed for the terrestrial networks with rather static topologies, and may not be appropriate for the next-generation LEO satellite broadband networks (LSBNs) featured with high dynamicity. This paper presents a multi-agent deep reinforcement learning (MADRL) algorithm for making edge computing multi-level offloading decisions in the LSBNs. Particularly, computing offloading is formulated as a partially observable Markov decision process (POMDP) based multi-agent decision problem. Each LEO satellite is an intelligent agent, either conducting a received edge computing task or forwarding it to its four neighboring satellite or the nearest cloud node on the ground. These agents are fully cooperative and their deep neural network models used to make offloading decisions share the same parameter values and are trained by the same replay buffer. A centralized training and distributed execution framework is utilized to ensure that globally optimized offloading decisions can be achieved based on local observations. Comparative simulation experiments for six representative offloading approaches show that the proposed MADRL aided approach outperforms the others regarding to decreasing edge computing task processing delay and increasing onboard compute resource utilization ratio. In addition, the convergence of this MADRL aided approach is also the best among the three DRL-based approaches.

Conventional computation offloading approaches are originally designed for ground networks, and are not effective for low earth orbit (LEO) satellite networks. This paper proposes a multi-agent deep reinforcement learning (MADRL) algorithm for making multi-level offloading decisions in LEO satellite networks. Offloading is formulated as a partially observable Markov decision process based multi-agent decision problem. Each satellite as an agent either conducts a received task, forwards it to neighbors, or sends it to ground clouds based on its own policy. These agents are independent and their deep neural networks to make offloading decisions share identical parameter values and are trained by using the same replay buffer. A centralized training and distributed executing mechanism is adopted to ensure that agents can make globally optimized offloading decisions. Comparative experiments demonstrate that the proposed MADRL algorithm outperforms the five baselines in terms of task processing delay and bandwidth consumption with acceptable computational complexity.