Decentralized federated learning (DFL) is an emerging paradigm to enable edge devices collaboratively training a learning model using a device-to-device (D2D) communication manner without the coordination of a parameter server (PS). Aggregation weights, also known as mixing weights, are crucial in DFL process, and impact the learning efficiency and accuracy. Conventional design relies on a so-called central entity to collect all local information and conduct system optimization to obtain appropriate weights. In this paper, we develop a distributed aggregation weight optimization algorithm to align with the decentralized nature of DFL. We analyze convergence by quantitatively capturing the impact of the aggregation weights over decentralized communication networks. Based on the analysis, we then formulate a learning performance optimization problem by designing the aggregation weights to minimize the derived convergence bound. The optimization problem is further transformed as an eigenvalue optimization problem and solved by our proposed subgradient-based algorithm in a distributed fashion. In our algorithm, edge devices only need local information to obtain the optimal aggregation weights through local (D2D) communications, just like the learning itself. Therefore, the optimization, communication, and learning process can be all conducted in a distributed fashion, which leads to a genuinely distributed DFL system. Numerical results demonstrate the superiority of the proposed algorithm in practical DFL deployment.

Decentralized machine learning (DML) supports collaborative training in large-scale networks with no central server. It is sensitive to the quality and reliability of inter-device communications that result in time-varying and stochastic topologies. This paper studies the impact of unreliable communication on the convergence of DML and establishes a direct connection between the spectral properties of the mixing process and the global performance. We provide rigorous convergence guarantees under random topologies and derive bounds that characterize the impact of the expected mixing matrix's spectral properties on learning. We formulate a spectral optimization problem that minimizes the nontrivial spectral radius of the expected second-order mixing matrix to enhance the convergence rate under probabilistic link failures. To solve this non-smooth spectral problem in a fully decentralized manner, we design an efficient subgradient-based algorithm that integrates Chebyshev-accelerated eigenvector estimation with local update and aggregation weight adjustment, while ensuring symmetry and stochasticity constraints without central coordination. Experiments on a realistic low Earth orbit satellite constellation with time-varying inter-satellite link models and real-world remote sensing data demonstrate the feasibility and effectiveness of our method. The method significantly improves classification accuracy and convergence efficiency compared to existing baselines, validating its applicability in satellite and other decentralized systems.

To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML). By collectively training a unified learning model on edge devices, FL bypasses the need of direct data transmission, thereby addressing problems such as latency issues and privacy concerns inherent in centralized ML. However, in practical deployment FL suffers from low learning efficiency due to the involved straggler issue and huge uplink overhead. In this paper, we develop a UAV-enabled over-the-air asynchronous FL (UAV-AFL) framework to address this problem. This framework significantly enhance the learning efficiency by supporting the UAV as the parameter server (UAV-PS) in collecting data over-the-air and updating model continuously. We conduct a convergence analysis to quantitatively capture the impact of model asynchrony, device selection and communication errors on the UAV-AFL learning efficiency. Based on this analysis, a unified communication-learning problem is formulated to maximize asymptotical learning accuracy by optimizing the UAV-PS trajectory, device selection and over-the-air transceiver design. Simulation results reveal valuable insights for the system design and demonstrate that the proposed UAV-AFL scheme achieves substantially improvement in learning efficiency compared with the state-of-the-art approaches.

Decentralized federated learning (DFL) is an emerging paradigm for leveraging the rapidly growing data from wireless devices in a fully distributed manner. However, the deployment of DFL is facing some pivotal challenges, including communication bottlenecks due to extensive inter-device message exchanges and the difficulty for edge devices to achieve consensus. To address these challenges, this paper proposes to employ the over-the-air computation (Aircomp) technique to improve communication efficiency and introduces a mixing matrix mechanism to guarantee consensus. Specifically, we present a novel multiple-input multiple-output over-the-air DFL (MIMO OA-DFL) framework for addressing the DFL design problem in general ad hoc networks. A rigorous convergence bound is derived to quantitatively capture the impact of mixing matrix and communication error on the system performance. The results show that the communication errors, the spectral gap of the mixing matrix, and the mixing matrix itself have a significant impact on the learning performance. Building on this result, we formulate a joint communication-learning optimization problem to optimize transceiver beamformers and mixing matrix. Numerical experiments demonstrate the substantial performance enhancement achieved by our proposed scheme.

Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner. With the cooperation of edge devices, DFL enables joint training of machine learning model in a device to device (D2D) communication fashion without the coordination of a parameter server. However, the deployment of wireless DFL is facing some pivotal challenges. Communication is a critical bottleneck due to the required extensive message exchanges between neighbor devices to share the learned model. Besides, model consensus becomes increasingly difficult as the number of devices grows because there is no available central server for coordination. To overcome these difficulties, this paper proposes the use of over-the-air computation (Aircomp) to improve communication efficiency by exploiting the superposition property of analog waveforms in multi-access channels, and introduce the mixing matrix mechanism to promote consensus using the spectral property of symmetric doubly stochastic matrix. Specifically, we develop a novel multiple-input multiple-output (MIMO) over-the-air DFL (OA-DFL) framework to study over-the-air DFL problem over MIMO multiple access channels. We conduct a general convergence analysis to quantitatively capture the impact of aggregation weights and communication error on the MIMO OA-DFL performance inad hocD2D networks. The result shows that the communication error together with the spectral gap of the mixing matrix has a significant impact on the learning performance. Based on this, a joint communication-learning optimization problem is formulated to optimize the transceiver beamformers and the mixing matrix. Extensive numerical experiments are performed to reveal the characteristics of different topologies and demonstrate the substantial learning performance enhancement of our proposed algorithm.

This paper presents a new three-dimensional (3D) trajectory design approach for a solar-powered, fixed-wing unmanned aerial vehicle (UAV) to harvest solar energy and collect data from multiple smart devices (SDs). The trajectory is optimized based on max-min fairness to balance the total amount of uploaded data and the fairness among the SDs. The key idea is that we develop non-trivial variable substitution and successive convex approximation (SCA) techniques to convexify data transmission, UAV energy consumption and mobility, and energy harvesting constraints under a persistent round-robin transmission schedule of the SDs. The resulting algorithm guarantees a locally optimal trajectory satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that we further jointly optimize the transmission schedule along with the trajectory, and prove that absolute fairness in terms of uploaded data can be achieved among the SDs under the max-min fairness. The new algorithms apply to both line-of-sight (LoS)-dominant and probabilistic SD-UAV channels. Numerical results show that a 3D trajectory increases the uploaded data by 111%, compared to a two-dimensional (2D) trajectory. The proposed algorithms can balance the energy harvesting and data collection, and achieve fairness in both LoS-dominant and probabilistic SD-UAV channels.