Erwu Liu

Pengcheng Sun, Erwu Liu, Wei Ni et al.

Non-independent and identically distributed (Non- IID) data adversely affects federated learning (FL) while heterogeneity in communication quality can undermine the reliability of model parameter transmission, potentially degrading wireless FL convergence. This paper proposes a novel dual-segment clustering (DSC) strategy that jointly addresses communication and data heterogeneity in FL. This is achieved by defining a new signal-to-noise ratio (SNR) matrix and information quantity matrix to capture the communication and data heterogeneity, respectively. The celebrated affinity propagation algorithm is leveraged to iteratively refine the clustering of clients based on the newly defined matrices effectively enhancing model aggregation in heterogeneous environments. The convergence analysis and experimental results show that the DSC strategy can improve the convergence rate of wireless FL and demonstrate superior accuracy in heterogeneous environments compared to classical clustering methods.

Pengcheng Sun, Erwu Liu, Wei Ni et al.

The aggregation efficiency and accuracy of wireless Federated Learning (FL) are significantly affected by resource constraints, especially in heterogeneous environments where devices exhibit distinct data distributions and communication capabilities. This paper proposes a clustering strategy that leverages prior knowledge similarity to group devices with similar data and communication characteristics, mitigating performance degradation from heterogeneity. On this basis, a novel Cluster- Aware Multi-round Update (CAMU) strategy is proposed, which treats clusters as the basic units and adjusts the local update frequency based on the clustered contribution threshold, effectively reducing update bias and enhancing aggregation accuracy. The theoretical convergence of the CAMU strategy is rigorously validated. Meanwhile, based on the convergence upper bound, the local update frequency and transmission power of each cluster are jointly optimized to achieve an optimal balance between computation and communication resources under constrained conditions, significantly improving the convergence efficiency of FL. Experimental results demonstrate that the proposed method effectively improves the model performance of FL in heterogeneous environments and achieves a better balance between communication cost and computational load under limited resources.

Rui Sun, Zhipeng Wang, Hengrui Zhang et al.

One of the biggest challenges of building artificial intelligence (AI) model in the healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for modelling is exhausting, costly, and sometimes impossible. In this paper, we propose a framework for global healthcare modelling using datasets from multi-continents (Europe, North America, and Asia) without sharing the local datasets, and choose glucose management as a study model to verify its effectiveness. Technically, blockchain-enabled federated learning is implemented with adaptation to meet the privacy and safety requirements of healthcare data, meanwhile, it rewards honest participation and penalizes malicious activities using its on-chain incentive mechanism. Experimental results show that the proposed framework is effective, efficient, and privacy-preserving. Its prediction accuracy consistently outperforms models trained on limited personal data and achieves comparable or even slightly better results than centralized training in certain scenarios, all while preserving data privacy. This work paves the way for international collaborations on healthcare projects, where additional data is crucial for reducing bias and providing benefits to humanity.

Pengcheng Sun, Erwu Liu, Rui Wang

The quality of wireless communication will directly affect the performance of federated learning (FL), so this paper analyze the influence of wireless communication on FL through symbol error rate (SER). In FL system, non-orthogonal multiple access (NOMA) can be used as the basic communication framework to reduce the communication congestion and interference caused by multiple users, which takes advantage of the superposition characteristics of wireless channels. The Minimum Mean Square Error (MMSE) based serial interference cancellation (SIC) technology is used to recover the gradient of each terminal node one by one at the receiving end. In this paper, the gradient parameters are quantized into multiple bits to retain more gradient information to the maximum extent and to improve the tolerance of transmission errors. On this basis, we designed the SER-based device selection mechanism (SER-DSM) to ensure that the learning performance is not affected by users with bad communication conditions, while accommodating as many users as possible to participate in the learning process, which is inclusive to a certain extent. The experiments show the influence of multi-bit quantization of gradient on FL and the necessity and superiority of the proposed SER-based device selection mechanism.

Rui Wang, Heju Li, Erwu Liu

The rapid increase of the data scale in Internet of Vehicles (IoV) system paradigm, hews out new possibilities in boosting the service quality for the emerging applications through data sharing. Nevertheless, privacy concerns are major bottlenecks for data providers to share private data in traditional IoV networks. To this end, federated learning (FL) as an emerging learning paradigm, where data providers only send local model updates trained on their local raw data rather than upload any raw data, has been recently proposed to build a privacy-preserving data sharing models. Unfortunately, by analyzing on the differences of uploaded local model updates from data providers, private information can still be divulged, and performance of the system cannot be guaranteed when partial federated nodes executes malicious behavior. Additionally, traditional cloud-based FL poses challenges to the communication overhead with the rapid increase of terminal equipment in IoV system. All these issues inspire us to propose an autonomous blockchain empowered privacy-preserving FL framework in this paper, where the mobile edge computing (MEC) technology was naturally integrated in IoV system.

Yuanzhe Geng, Erwu Liu, Rui Wang et al.

Cooperative communication is an effective approach to improve spectrum utilization. In order to reduce outage probability of communication system, most studies propose various schemes for relay selection and power allocation, which are based on the assumption of channel state information (CSI). However, it is difficult to get an accurate CSI in practice. In this paper, we study the outage probability minimizing problem subjected to a total transmission power constraint in a two-hop cooperative relay network. We use reinforcement learning (RL) methods to learn strategies for relay selection and power allocation, which do not need any prior knowledge of CSI but simply rely on the interaction with communication environment. It is noted that conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further propose a hierarchical reinforcement learning (HRL) framework and training algorithm. A key difference from other RL-based methods in existing literatures is that, our proposed HRL approach decomposes relay selection and power allocation into two hierarchical optimization objectives, which are trained in different levels. With the simplification of search space, the HRL approach can solve the problem of sparse reward, while the conventional RL method fails. Simulation results reveal that compared with traditional DRL method, the HRL training algorithm can reach convergence 30 training iterations earlier and reduce the outage probability by 5% in two-hop relay network with the same outage threshold.

Yuanzhe Geng, Erwu Liu, Rui Wang et al.

Route planning is important in transportation. Existing works focus on finding the shortest path solution or using metrics such as safety and energy consumption to determine the planning. It is noted that most of these studies rely on prior knowledge of road network, which may be not available in certain situations. In this paper, we design a route planning algorithm based on deep reinforcement learning (DRL) for pedestrians. We use travel time consumption as the metric, and plan the route by predicting pedestrian flow in the road network. We put an agent, which is an intelligent robot, on a virtual map. Different from previous studies, our approach assumes that the agent does not need any prior information about road network, but simply relies on the interaction with the environment. We propose a dynamically adjustable route planning (DARP) algorithm, where the agent learns strategies through a dueling deep Q network to avoid congested roads. Simulation results show that the DARP algorithm saves 52% of the time under congestion condition when compared with traditional shortest path planning algorithms.