Decentralized bilevel optimization has garnered significant attention due to its critical role in solving large-scale machine learning problems. However, existing methods often rely on prior knowledge of problem parameters—such as smoothness, convexity, or communication network topologies—to determine appropriate stepsizes. In practice, these problem parameters are typically unavailable, leading to substantial manual effort for hyperparameter tuning. In this paper, we propose \textbf{AdaSDBO}, a fully problem-parameter-free algorithm for decentralized bilevel optimization with a single-loop structure. AdaSDBO leverages adaptive stepsizes based on cumulative gradient norms to update all variables simultaneously, dynamically adjusting its progress and eliminating the need for problem-specific hyperparameter tuning. Through rigorous theoretical analysis, we establish that AdaSDBO achieves a convergence rate of $\widetilde{\mathcal{O}}\left(\frac{1}{T}\right)$, matching the performance of well-tuned state-of-the-art methods up to polylogarithmic factors. Extensive numerical experiments demonstrate that AdaSDBO delivers competitive performance compared to existing decentralized bilevel optimization methods while exhibiting remarkable robustness across diverse stepsize configurations.

As a promising paradigm, federated learning has been applied to multimodal sensing tasks due to its deployment convenience. However, the recent advances in multimodal federated learning emphasize learning a high-quality multimodal model but overlook the model usage requirements of massive unimodal clients. Moreover, the privacy risk in model sharing and client data heterogeneity impact the efficacy of federated learning. In this paper, we propose a novel multimodal federated learning system named MASA. As a departure from existing approaches, MASA simultaneously enhances the model learning efficiency of both multimodal and unimodal clients while ensuring their data privacy. First, we employ a gated cross-modal distillation scheme to achieve performance-aware knowledge transfer across modality-heterogeneous clients. To enhance the system security, MASA integrates a lightweight split-shuffle mechanism to realize the anonymization and encryption of model aggregation. Moreover, to reach personalized collaboration while protecting privacy, MASA features an attention-based spontaneous client clustering mechanism to form client cluster structures securely and distributedly. We evaluate our MASA on four public multimodal datasets for human activity recognition. The results show that our MASA outperforms leading multimodal federated learning methods on the model performance of both multimodal and unimodal clients.

Rapid advances in low Earth orbit (LEO) satellite technology and satellite edge computing (SEC) have facilitated a key role for LEO satellites in enhanced Earth observation missions (EOM). These missions (e.g., remote object detection) typically require multi-satellite cooperative observations of a large region of interest (RoI) area, as well as the observation image routing and computation processing, enabling accurate and real-time responsiveness. However, optimizing the resources of LEO satellite networks is nontrivial in the presence of its dynamic and heterogeneous properties. To this end, we propose SECO, a SEC-enabled framework that jointly optimizes multi-satellite observation scheduling, routing and computation node selection for enhanced EOM. Specifically, in the observation phase, we leverage the orbital motion and the rotatable onboard cameras of satellites, and propose a distributed game-based scheduling strategy to minimize the overall size of captured images while ensuring full (observation) coverage. In the sequent routing and computation phase, we first adopt image splitting technology to achieve parallel transmission and computation. Then, we propose an efficient iterative algorithm to jointly optimize image splitting, routing and computation node selection for each captured image. On this basis, we propose a theoretically guaranteed systemwide greedy-based strategy to reduce the total time cost (i.e., transmission, computation and queuing delay) over simultaneous processing for multiple images. Extensive experiments based on real-world datasets demonstrate that SECO can achieve up to a 60.7% reduction in overall time cost compared to baselines.

Low Earth orbit (LEO) satellites enable complex Earth observation tasks (e.g.,remote sensing and cooperative monitoring) by leveraging large-scale satellite-generated Earth imageries and state-of-the-art machine learning (ML) techniques. However, due to restricted downlink bandwidth and spotty connectivity, it is infeasible for the satellites to transmit all the imageries to ground stations for ML model training. To address this issue, we use federated learning (FL) to mitigate the significant overhead of raw data transmission only by enabling model parameter exchange. Traditional FL requires a central server for model parameter aggregation, which is impractical for distributed LEO satellite constellation due to the difficulty of identifying a suitable central satellite. To tackle such challenge, we take the unique topological characteristics of the LEO satellite constellation to design a decentralized FL framework that enables efficient model aggregation in LEO satellite networks without a central server. The framework can avoid the reliability and communication bandwidth problems of the central server in centralized FL. To mitigate the straggler effect and address the statistical heterogeneity, we then propose a novel offloading framework for decentralized FL in LEO satellite networks to aid the collaboration among multiple satellites for resource sharing. Based on it, we derive a satellite-centric threshold-based offloading strategy and a system-wide greedy-based iterative offloading decision making algorithm, in order to achieve delay and accuracy optimization under the computation and communication power constraints. Theoretical analysis demonstrates that the proposed framework contributes to the high training performance of the global model. Extensive experiments based on realistic datasets show that the proposed framework can reduce the system delay by up to 41% on average and improve the global model accuracy by up to 9.39% compared with benchmark policies.

The space-air-ground integrated network (SAGIN), one of the key technologies for next-generation mobile communication systems, can facilitate data transmission for users all over the world, especially in some remote areas where vast amounts of informative data are collected by Internet of remote things (IoRT) devices to support various data-driven artificial intelligence (AI) services. However, training AI models centrally with the assistance of SAGIN faces the challenges of highly constrained network topology, inefficient data transmission, and privacy issues. To tackle these challenges, we first propose a novel topology-aware federated learning framework for the SAGIN, namely Olive Branch Learning (OBL). Specifically, the IoRT devices in the ground layer leverage their private data to perform model training locally, while the air nodes in the air layer and the ring-structured low earth orbit (LEO) satellite constellation in the space layer are in charge of model aggregation (synchronization) at different scales. To further enhance communication efficiency and inference performance of OBL, an efficient Communication and Non-IID-aware Air node-Satellite Assignment (CNASA) algorithm is designed by taking the data class distribution of the air nodes as well as their geographic locations into account. Furthermore, we extend our OBL framework and CNASA algorithm to adapt to more complex multi-orbit satellite networks. We analyze the convergence of our OBL framework and conclude that the CNASA algorithm contributes to the fast convergence of the global model. Extensive experiments based on realistic datasets corroborate the superior performance of our algorithm over the benchmark policies.