As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme.

Computation offloading involves transmitting compute-intensive tasks from mobile users (MUs) to edge environments. This compensates for the limitations of terminal devices in computing performance and resource storage capabilities. However, with the growing demand for offloading compute-intensive tasks, the burden on edge server networks has intensified. On the other hand, the response time of application has increased, leading to a decline in user experience. Therefore, selecting a reliability metric caused by congestion becomes an important issue for analyzing the quantitative characteristics of edge networks. The current research primarily focuses on the precise validation offloading task results. Typically, methods like task redistribution and third-party assistance are employed to ensure task reliability. However, these measures lead to increased time and energy consumption. In this paper, we propose a more versatile reliability metric based on the probability distribution of average waiting time in a multi-queue model. Additionally, to incentive MUs to offload more tasks and enhance the economic utility of edge server providers (ESPs), we employ a Stackelberg game to model the dynamic interaction between ESPs and MUs. Finally, we utilize the alternating direction method of multipliers (ADMM) algorithm to derive the optimal strategies for ESPs and MUs. Simulation results demonstrate that our proposed approach surpasses the baselines in terms of reliability indicator. Moreover, it achieves faster convergence and decision-making in comparison to conventional heuristic methods.

Industry 5.0 facilitates the intelligent upgrade of smart factories in Industrial Internet of Things (IIoT), and also introduces a plethora of data processing challenges. Mobile edge computing offloads data to edge servers for processing, easing the data processing pressure and reducing system cost. However, smart factories contain numerous sensitive information, and offloading them to edge servers directly may pose a risk of data leakage. To address these challenges, we investigate a localedge collaborative smart factory system. Specifically, we firstly model the tasks as a directed acyclic graph, and formulate the offloading problem as a Markov decision process, considering the optimization of latency, energy consumption and the number of overtime tasks. Then, we propose a security-aware computation offloading method using federated reinforcement learning in IIoT, named SCOF. SCOF employs federated learning, keeping data for local computation and uploading model parameters to edge servers for aggregation. The data transmission passes through an artificial noise channel to protect against eavesdropping. Meanwhile, SCOF utilizes differential privacy to protect data security and employs deep reinforcement learning for selecting near-optimal offloading decisions. Finally, abundant experiments are conducted under a real dataset. The results show that SCOF has better perfomance than the state-of-the-art baseline algorithms.

With the rapid growth of the Industrial Internet of Things, a large amount of industrial data that needs to be processed promptly. Edge computing-based computation offloading can well assist industrial devices to process these data and reduce the overall time overhead. However, there are dependencies among tasks and some tasks have high latency requirements, so completing computation offloading while considering the above factors faces important challenges. In this paper, we design a computation offloading method based on a directed acyclic graph task model by modeling task dependencies. In addition to considering traditional optimization objectives in previous computation offloading problems (e.g., latency, energy consumption, etc.), we also propose an age of information (AoI) model to reflect the freshness of information and transform the task offloading problem into an optimization problem for latency, energy consumption, and AoI. To address this issue, we propose a method based on an improved dueling double deep Q-network computation offloading algorithm, named ID3CO. Specifically, it combines the advantages of deep Q-network, double deep Q-network, and dueling deep Q-network algorithms while further utilizing deep residual neural networks to improve convergence. Extensive simulations are conducted to demonstrate that ID3CO outperforms the existing baselines in terms of performance.