With the surge in the number of low earth orbit (LEO) satellites, continuous research has emerged on using satellite data to train artificial intelligence models. On one hand, traditional centralized training on the ground is not feasible due to privacy concerns and limited bandwidth for downloading raw satellite data. On the other hand, due to the limited energy and computational capability of satellites, training directly on satellites suffers from prolonged latency, especially for large models. To alleviate these issues, we propose a novel satellite-ground collaborative federated fine-tuning architecture, where ground stations (GSs) and satellites collaboratively train a global model without the need for data downloads. In this proposed architecture, satellites serve as edge devices and the ground server serves as a coordinator. However, the short satellite-ground communication windows caused by the high mobility of satellites and the substantial intra-orbit data transmission bring special challenges to the transmission process of federated edge learning. To tackle these challenges, we carefully design the satellite-ground collaborative fine-tuning architecture and utilize an optimized ring all-reduce algorithm and network flow algorithm to enhance the intra-orbit and ground-satellite transmissions, respectively. Experimental results demonstrate that our proposed architecture significantly reduces the training time by 40% compared to training solely on satellite.

Federated learning (FL) is an emerging technique that enables privacy-preserving distributed learning. Most related works focus on centralized FL, which leverages the coordination of a parameter server to implement local model aggregation. However, this scheme heavily relies on the parameter server, which could cause scalability, communication, and reliability issues. To tackle these problems, decentralized FL, where information is shared through gossip, starts to attract attention. Nevertheless, current research mainly relies on first-order optimization methods that have a relatively slow convergence rate, which leads to excessive communication rounds in wireless networks. To design communication-efficient decentralized FL, we propose a novel over-the-air decentralized second-order federated algorithm. Benefiting from the fast convergence rate of the second-order method, total communication rounds are significantly reduced. Meanwhile, owing to the low-latency model aggregation enabled by over-the-air computation, the communication overheads in each round can also be greatly decreased. The convergence behavior of our approach is then analyzed. The result reveals an error term, which involves a cumulative noise effect, in each iteration. To mitigate the impact of this error term, we conduct system optimization from the perspective of the accumulative term and the individual term, respectively. Numerical experiments demonstrate the superiority of our proposed approach and the effectiveness of system optimization.

To tackle the issue of heterogeneous input raw data samples obtained by different devices and enhance the feature extraction capability of edge devices, we propose a vertically split neural network based edge-device collaborative artificial intelligence (AI) inference framework. The local results calculated by various light-size sub-networks at edge devices are transmitted and aggregated at the server for the downstream inference task. Nevertheless, the transmission of such high-dimensional local results involves severe communication overhead. To resolve this issue, the technique of over-the-air computation (AirComp) is adopted to enable low-latency aggregation. The same entry of all devices’ local results is transmitted over a same wireless resource block and aggregated via the waveform superposition property. Furthermore, to simultaneously support the aggregation of all dimensions of the local results, we consider a broadband channel and leverage orthogonal frequency division multiplexing (OFDM) to divide the system bandwidth into multiple subcarriers which are then assigned for different dimensions. Consequently, an extra degree of freedom is introduced to design the aggregation of all dimensions. We then propose a scheme of joint subcarrier allocation, power allocation, and receiver beamforming to minimize the aggregation distortion and enhance inference performance. Extensive experiments are conducted to verify the superiority of the proposed design over benchmarks.

As an efficient technique for resource sharing in data centers, network virtualization enables resource multiplexing by allowing multiple heterogeneous virtual networks (VNs) to simultaneously coexist on the shared substrate infrastructure. How to effectively embed the VNs onto the substrate network is known as the virtual network embedding (VNE) problem. However, as an NP-hard problem, the VNE problem-solving suffers a high computation complexity. Artificial Intelligence (AI) provides a promising way to alleviate these issues. However, the existing AI-based works still cannot fully and efficiently leverage substrate network information to formulate embedding policies. To this end, in this paper we propose a novel deep reinforcement learning (DRL) based coordinated VNE algorithm, called Intelligent Coordinated Embedding (ICE). To reduce the computation complexity, ICE adopts an efficient resource abstraction model, Blocking Island (BI), which greatly reduces the search space. With the benefit of DRL and BI, ICE can efficiently adjust embedding strategies according to the environment states, aiming to maximize resource utilization and overall revenue while minimizing the embedding cost. The experimental results prove that ICE outperforms both the traditional non-DRL-based approach and the state-of-the-art DRL-based approach.

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users’ data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic flows over wireless networks with limited radio resources. To design communication-efficient FL, most of the existing studies employ the first-order federated optimization approach that has a slow convergence rate. This however results in excessive communication rounds for local model updates between the edge devices and edge server. To address this issue, in this paper, we instead propose a novel over-the-air second-order federated optimization algorithm to simultaneously reduce the communication rounds and enable low-latency global model aggregation. This is achieved by exploiting the waveform superposition property of a multi-access channel to implement the distributed second-order optimization algorithm over wireless networks. The convergence behavior of the proposed algorithm is further characterized, which reveals a linear-quadratic convergence rate with an accumulative error term in each iteration. We thus propose a system optimization approach to minimize the accumulated error gap by joint device selection and beamforming design. Numerical results demonstrate the system and communication efficiency compared with the state-of-the-art approaches.