Swapping and purification are the two fundamental building blocks for high-fidelity entanglement distribution in multi-hop quantum networks. Unfortunately, it is still a mystery how they intertwine with each other to affect the fidelity and cost of end-to-end entanglements. Current scheduling algorithms consider this problem under relatively limited assumptions and a critical yet unjustified conjecture. In this work, we first consider more general assumptions with operation failures and, accordingly, extend a tree-based modeling for joint swapping and purification. Then, we analytically prove the previous conjecture that the optimal strategy underBinary systemis always to purify the entanglements before any swapping. This sheds light on the protocol and device design for quantum networks. We then further propose a tree-based algorithm, which can efficiently schedule swapping and purification along a path for bothBinaryandWerner systems. Extensive simulations of the proposed method against state-of-the-art solutions show that our method uses fewer entanglements to establish qualified end-to-end entanglements, and thus achieves higher network throughput.

Space-air-ground integrated networks can provide better connectivity and improved performance to ground users and thus have been well-recognized as the innovative trend for future 6G and beyond wireless systems. However, such heterogeneous and complex integrated communication systems also pose many new challenges for joint performance optimization. In this paper, we study the data delivery optimization problem in a space-air-ground network, where joint resource management decisions on user association, bandwidth allocation, cache placement, and high-altitude platform (HAP) location selection have to be optimized. To tackle this complex and challenging mixed integer programming optimization problem, we introduce a quantum-assisted method, named the Hybrid quantum-classical Benders’ Decomposition (HyBD) algorithm, which leverages the strengths of both quantum and classical computing. Experiments on the commercial quantum annealing machine demonstrate the effectiveness and robustness of the proposed HyBD method, with up to 64.3% improvement of iteration number and 82.8% improvement of average computation time over the classical Benders’ Decomposition algorithm on classical CPUs even at small scales, which demonstrates the quantum advantage.

Incentivizing participation and coordinating decisions across hierarchical agents remain critical challenges in blockchain-enabled federated learning (FL) over edge clouds, especially when a coded FL is performed over a client-edge-cloud hierarchical system. This paper proposes a novel hybrid incentive framework that integrates multidimensional contract theory with reinforcement learning (RL)-based Stackelberg game modeling for such a system. Specifically, we design personalized contracts between edge servers and clients, addressing their heterogeneous data volume, privacy sensitivity, and computational capacity under incomplete information. Simultaneously, we model the task publisher's reward allocation to edge servers as a one-leader multi-follower Stackelberg game, where each follower acts based on local observations. A decentralized RL algorithm is proposed to learn optimal reward strategies without revealing other agents' private information, such as local data volume/quality. Simulations demonstrate that our method can converge to equilibrium and achieve effectiveness under incomplete information compared to baseline incentive schemes.

Swapping and purification are the two fundamental building blocks for multi-hop quantum networks. However, their interplay and its impact on end-to-end fidelity and cost are not yet fully explored. Existing scheduling algorithms address this problem under certain simplified assumptions and models that may not fully capture the complexities of real scenarios. In this work, we first consider more general assumptions that account for operation failures and extend a tree-based modeling approach for joint swapping and purification. Then, for the first time, we analytically prove the previous conjecture that the optimal strategy under Binary system is always to purify the entanglements before any swapping. This sheds light on the protocol and device design for entanglement distribution in quantum networks. We then further propose a tree-based algorithm, which can efficiently schedule swapping and purification along a path for both Binary and Werner systems. Extensive simulations have been conducted to evaluate the proposed method against the existing solutions, and the results show that our method uses fewer entanglements to establish qualified end-to-end entanglements and thus achieves higher network throughput.

Mobile edge computing (MEC) has revolutionized the way computational tasks are offloaded and latency is reduced by leveraging edge servers close to end devices. Efficient resource allocation and task offloading are crucial for enhancing system performance in MEC environments. Traditional reinforcement learning (RL) approaches have shown promise in optimizing resource allocation and task offloading problems. However, they often face challenges such as high computational complexity and the need for extensive training data. Quantum reinforcement learning (QRL) emerges as a promising solution to overcome these limitations by leveraging quantum computing principles to enhance efficiency and scalability. In this paper, we propose a hybrid quantum-classical non-sequential model for joint resource allocation and task offloading in MEC systems. Our model combines the advantages of RL in handling environmental dynamics and quantum computing in reducing adjustable parameters and accelerating the training process. Extensive experiments demonstrate that our proposed algorithm can achieve higher training and inference performance under various parameter settings compared to traditional RL models and previous QRL models.

Numerous optimization scenarios such as industrial production planning, network communication routing, and logistic scheduling can be modeled as large-scale integer linear programming problems. However, due to the NP-Hardness of these problems, it is very challenging to optimally solve these problems in a short time on classical computers. Quantum computers have emerged as a new computing platform to provide new computing paradigms to tackle these problems. However, the scalability and efficiency of current quantum computers pose significant challenges in practical implementations of quantum optimization algorithms. In this paper, we propose a novel hybrid quantum-classical approach, termed Hybrid quantum-classical Dantzig-Wolfe Decomposition (HyDWD), aimed at solving these problems. In this framework, the subproblems can be solved in parallel on quantum computers. Our results demonstrate the benefits of integrating parallel quantum computing with the proposed hybrid quantum-classical framework via Dantzig-Wolfe decomposition, paving the way for advancements in optimization and decision-making processes.

Topology is one of the most critical properties of networks. Quantum networks, as a new type of network, have fundamentally different principles for establishing connections compared to classical networks, leading to distinct challenges in topology design. Finding the optimal topology for quantum networks to meet traffic demands is a crucial yet not fully understood problem. In this paper, we explore the topology design problem for quantum networks, considering both resource allocation and entanglement distribution. We propose and investigate both flow-based and path-based formulations, along with their associated solutions, aimed at minimizing the topology cost. For the path-based formulation, we also provide the first theoretical analysis of the cost associated with swapping strategies over a quantum path. Extensive simulations demonstrate that our enhanced path-based formulation is both efficient and effective.

In a blockchain-based federated learning (FL) framework, clients can contribute private data or computing resources to the overall FL training or mining task. To overcome the impractical assumption that participants will voluntarily join training or mining, it is crucial to design an incentive mechanism that motivates participants to achieve optimal training and mining outcomes. In this paper, we investigate the incentive mechanism design for a semi-asynchronous blockchain-based FL system. We model the resource pricing mechanism among clients and task publishers as a Stackelberg game, and prove the existence and uniqueness of a Nash equilibrium in such a game. We then propose an iterative algorithm based on the Alternating Direction Method of Multipliers (ADMM) to achieve the optimal strategies for each participant. Finally, our simulation results verify the convergence and efficiency of our proposed scheme.

Hierarchical federated learning has emerged as a pragmatic approach to addressing scalability, robustness, and privacy concerns within distributed machine learning, particularly in the context of edge computing. This hierarchical method involves grouping clients at the edge, where the constitution of client groups significantly impacts overall learning performance, influenced by both the benefits obtained and costs incurred during group operations (such as group formation and group training). This is especially true for edge and mobile devices, which are more sensitive to computation and communication overheads. The formation of groups is critical for group-based hierarchical federated learning but often neglected by researchers, especially in the realm of edge systems. In this paper, we present a comprehensive exploration of a group-based federated edge learning framework utilizing the hierarchical cloud-edge-client architecture and employing probabilistic group sampling. Our theoretical analysis of its convergence rate, considering the characteristics of client groups, reveals the pivotal role played by group heterogeneity in achieving convergence. Building on this insight, we introduce new methods for group formation and group sampling, aiming to mitigate data heterogeneity within groups and enhance the convergence and overall performance of federated learning. Our proposed methods are validated through extensive experiments, demonstrating their superiority over current algorithms in terms of prediction accuracy and training cost.

The advancement of satellite-based quantum networks shows promise in transforming global communication infrastructure by establishing a secure and reliable quantum Internet. These networks use optical signals from satellites to ground stations to distribute high-fidelity quantum entanglements over long distances, overcoming the limitations of traditional terrestrial systems. However, the complexity of satellite-based entanglement distribution and terrestrial quantum swapping in the integrated network requires joint optimization with satellite assignment, resource allocation, and path selection. To address this challenge, we introduce a hybrid quantum-classical algorithm to solve the optimization problem by leveraging the strengths of both quantum and classical computing. The original problem is decomposed into a master problem and several subproblems using Dantzig-Wolfe decomposition and linearization techniques. Through experiments, this study demonstrates the effectiveness and reliability of the proposed methods in optimizing large-scale networks and managing qubit usage compared to the classical optimization techniques. The findings provide valuable insights for designing and implementing satellite-based entanglement distribution in quantum networks, paving the way for a secure global quantum communication infrastructure.

Deploying federated learning (FL) in edge clouds poses challenges, especially when multiple models are concurrently trained in resource-constrained edge environments. Existing research on federated edge learning has predominantly focused on client selection for training a single FL model, typically with a fixed learning topology. Preliminary experiments indicate that FL models with adaptable topologies exhibit lower learning costs compared to those with fixed topologies. This paper delves into the intricacies of jointly selecting participants and learning topologies for multiple FL models simultaneously trained in the edge cloud. The problem is formulated as an integer non-linear programming problem, aiming to minimize total learning costs associated with all FL models while adhering to edge resource constraints. To tackle this challenging optimization problem, we introduce a two-stage algorithm that decouples the original problem into two sub-problems and iteratively addresses them separately with efficient heuristics. Our method enhances resource competition and load balancing in edge clouds by allowing FL models to choose participants and learning topologies independently. Extensive experiments conducted with real-world networks and FL datasets affirm the better performance of our algorithm, demonstrating lower average total costs with up to 33.5% and 39.6% compared to previous methods designed for multi-model FL.

The scheduling problem for federated learning (FL) with multiple models in a distributed network is challenging, as it involves NP-hard mixed-integer nonlinear programming. Moreover, it requires optimal participant selection and learning rate determination among multiple FL models to avoid high training costs and resource competition. To overcome those chal-lenges, in literature the Benders' decomposition algorithm (BD) can deal with mixed integer problems, however, it still suffers from limited scalability. To address this issue, in this paper, we present the Hybrid Quantum-Classical Benders' Decomposition (HQCBD) algorithm, which combines the power of quantum and classical computing to solve the joint participant selection and learning scheduling problem in multi-model FL. HQCBD decomposes the optimization problem into a master problem with binary variables and small subproblems with continuous variables. This collaboration maximizes the potential of both quantum and classical computing, and optimizes the complex joint optimization problem. Simulation on the commercial D-Wave quantum annealing machine demonstrates the effectiveness and robustness of the proposed method, with up to 18% improvement of iterations and 81% improvement of computation time over BD algorithm on classical CPUs even at small scales.

Hierarchical federated learning has been studied as a more practical approach to federated learning in terms of scalability, robustness, and privacy protection, particularly in edge computing. To achieve these advantages, operations are typically conducted in a grouped manner at the edge, which means that the formation of client groups can affect the learning performance, such as the benefits gained and costs incurred by group operations. This is especially true for edge and mobile devices, which are more sensitive to computation and communication overheads. The formation of groups is critical for group-based federated edge learning but has not been studied in detail, and even been overlooked by researchers. In this paper, we consider a group-based federated edge learning framework that leverages the hierarchical cloud-edge-client architecture and probabilistic group sampling. We first theoretically analyze the convergence rate with respect to the characteristics of the client groups, and find that group heterogeneity plays an important role in the convergence. Then, on the basis of this key observation, we propose new group formation and group sampling methods to reduce data heterogeneity within groups and to boost the convergence and performance of federated learning. Finally, our extensive experiments show that our methods outperform current algorithms in terms of prediction accuracy and training cost.

The proliferation of Internet of Things (IoT) data and innovative mobile services has promoted an increasing need for low-latency access to resources such as data and computing services. Mobile edge computing has become an effective computing paradigm to meet the requirement for low-latency access by placing resources and dispatching tasks at the edge clouds near mobile users. The key challenge of such solution is how to efficiently place resources and dispatch tasks in the edge clouds to meet the QoS of mobile users or maximize the platform’s utility. In this article, we study the joint optimization problem of resource placement and task dispatching in mobile edge clouds across multiple timescales under the dynamic status of edge servers. We first propose a two-stage iterative algorithm to solve the joint optimization problem in different timescales, which can handle the varieties among the dynamic of edge resources and/or tasks. We then propose a reinforcement learning (RL) based algorithm which leverages the learning capability of Deep Deterministic Policy Gradient (DDPG) technique to tackle the network variation and dynamic as well. The results from our trace-driven simulations demonstrate that both proposed approaches can effectively place resources and dispatching tasks across two timescales to maximize the total utility of all scheduled tasks.

In recent years, edge computing has become an increasingly popular computing paradigm to enable real-time data processing and mobile intelligence. Edge computing allows computing at the edge of the network, where data is generated and distributed at the nearby edge servers to reduce the data access latency and improve data processing efficiency. One of the key challenges in data-intensive edge computing is how to place the data at the edge clouds effectively such that the access latency to the data is minimized. In this paper, we study such a data placement problem in edge computing while different data items have diverse popularity. We propose a popularity based placement method which maps both data items and edge servers to a virtual plane and places or retrieves data based on its virtual coordinate in the plane. We then further propose additional placement strategies to handle load balancing among edge servers via either offloading or data duplication. Simulation results show that our proposed strategies efficiently reduce the average path length of data access and the load-balancing strategies indeed provide an effective relief of storage pressures at certain overloaded servers.

Handwritten signature authentication is an important service to defend against fraudulent activities. Current automated solutions rely heavily on dedicated devices and require certain user efforts. In this work, we explore the feasibility of a new type of signature authentication system, SAP - Signature Authentication with PPG Sensor, which leverages Photoplethysmography (PPG) sensors in wrist-worn wearable devices. To make SAP non-intrusive and secure, we design effective algorithms to separate the signature signals from the heartbeat signals in the raw PPG signals. We implement a low-cost hardware prototype of SAP. Our preliminary experimental results show that SAP can achieve an average F1 score of up to 98%.

Federated learning (FL) has been emerging as a new distributed machine learning paradigm recently. Although FL can protect the data privacy of participants by keeping their training data on local devices, there are recent works raising new privacy concerns especially when workers or the parameter server of FL are untrustworthy or malicious. One effective way to solve the problem is using hierarchical federated learning (HFL) where a few middle-layer aggregators (or called group leaders) are used to aggregate local model updates from workers and send group model updates to the parameter server. In this paper, we consider the participant selection problem of HFL in an edge cloud with multiple FL models, where each model needs to select one parameter server, a few group leaders and a certain amount of workers from edge servers to jointly perform HFL. We first formulate this problem as a non-linear integer programming, aiming to minimize the total learning cost of all models while satisfying the constrained edge resources. We then design a three-stage algorithm by decoupling the original problem into three sub-problems and solving them iteratively. Simulations with real-world datasets and FL models confirm that our proposed algorithm can efficiently reduce the average total learning cost in edge cloud compared with existing methods.

Handwritten signature authentication is a crucial service to defend against fraudulent activities. Existing automated solutions rely heavily on dedicated devices that are expensive and require different user efforts that affect the user experience. In this paper, we propose a new signature authentication system, PPGSign, which leverages Photoplethysmography (PPG) sensors in the existing wrist-worn wearable devices. The unique blood flow changes in the supplicant’s hand movement are exploited in this system to validate the signature. To make PPGSign nonintrusive and secure, we explore effective algorithms to separate the signature signals from the heartbeat signals in the raw PPG signals. We build a low-cost hardware prototype to verify our proposed method. Our experimental results show that PPGSign can achieve an average F1 score of up to 98%, which verifies the feasibility and efficiency of the proposed solution.

Edge computing has become an increasingly popular computing paradigm. Deploying edge clouds allows performing data-intensive computing at the edge of the network instead of a remote cloud to reduce data access latency and improve data processing efficiency. One of the key challenges in data-intensive edge computing is how to effectively place the data at the edge clouds such that the access latency to the data is minimized. In this paper, we study such a data placement problem in edge computing where different data items have diverse popularity. We first propose a data popularity based placement method when the data requests are unknown. It maps both data items and edge servers to a virtual plane and places data based on its virtual coordinate in the plane. We consider data popularity during both the mapping of data items to the plane and making the placement decision. We further propose an optimization-based placement strategy for the case when the data requests are known. By formulating an integer programming problem, our proposed solution aims to find the optimal placement decision. Simulation results show that both proposed strategies efficiently reduce the average latency of data access.

The proliferation of Internet of Things (IoT) data and innovative mobile services has promoted an increasing need for low-latency access to resources such as data and computing services. Mobile edge computing has become an effective computing paradigm to meet the requirement for low-latency access by placing resources and dispatching tasks at the network edge near mobile users. The key challenge of such solution is how to efficiently place resources and dispatch tasks to meet the QoS of mobile users or maximize the platform’s utility. In this paper, we study the joint optimization problem of resource placement and task dispatching in mobile edge computing across multiple timescales under the dynamic status of edge servers. We first propose a two-stage iterative algorithm to solve the joint optimization problem in different timescales, which can handle the varieties among the dynamic of edge resources and/or tasks. We then propose a reinforcement learning (RL) based algorithm which leverages the learning capability of Deep Deterministic Policy Gradient (DDPG) technique to tackle the network variation and dynamic as well. The results from trace-driven simulations demonstrate that our proposed approaches can effectively place resources and dispatching tasks across two timescales to maximize the total utility of all scheduled tasks.