Federated learning is a distributed learning approach that balances data privacy and collaborative learning. To address the impact of non-IID (non-Independently and Identically Distributed) data on the performance of the global model, this study delved into personalized federated learning. First, we introduced a local network with a self-attention mechanism, enhancing the personalization of local models. Next, we proposed a weighted average aggregation method, assigning weights based on the distance of the client model parameters and variations in contributions, mitigating the adverse effects of uneven data distribution. Using a dataset constructed from non-IID data partitions simulated via the Dirichlet distribution, we experimented, integrating the self-attention mechanism of the local model and the personalized aggregation strategy. Experimental results indicate that this approach significantly improves the test accuracy of the MNIST and SVHN datasets while reducing generalization error. In addition, it also reduces the oscillation during training, showing good robustness. In conclusion, this study offers fresh insights into handling non-IID data in federated learning and empirically validates the method’s effectiveness.
Multimodal sentiment analysis tasks have been extensively researched in recent years. However, real-world multimodal data collection is often problematic due to missing modalities, making it suboptimal. To address these issues, this paper presents a supervised contrast learning and cross-modal translation-based multimodal sentiment analysis model that can operate even when modalities are missing. First, supervised contrast learning is employed to carry out cluster analysis on the same sample batch and establish the connection between different samples. Incomplete features of the target samples are then supplemented with the other samples. Following that, a cross-modal translation network, based on Transformer, is utilised to learn the collective representation of different modalities. A feature reconstruction network is used, employing center distance difference loss, to reduce the difference between features obtained by the model under modality-missing and modality-complete conditions. This aims to enhance the robustness of feature extraction. Experimental findings from two publicly available datasets demonstrate the model’s improved performance for varying levels of modalities missing.
This paper introduces a novel approximate multiplier tailored for FPGAs, with a primary focus on its application in hardware accelerators for deep machine learning computations. In deep learning, multipliers represent a substantial portion of the hardware complexity. The primary objective is to enhance hardware efficiency compared to exact multipliers while maintaining an acceptable level of accuracy, especially for inference tasks. The proposed multiplier employs a parallel architecture featuring a configurable set of approximate adders. The paper provides comprehensive insights into the design of both INT8 and UINT8 configurations of this multiplier. The evaluations encompass error analysis findings, hardware implementation results, and accuracy measurements for the inference of a selected set of benchmark deep learning models. The results illustrate that a chosen configuration of this multiplier can achieve a notable 9% reduction in overall LUT utilization while incurring only an average 0.28% and 0.06% reduction in inference accuracy before and after approximation-aware retraining, respectively.
Parked vehicle assisted edge computing is effect to alleviate the resource supply-demand conflict in vehicular edge computing, by leveraging the idle resources in parked vehicles (PVs). The uncertain parking behavior and the load balance both of PVs are the critical factors to the quality-of-service (QoS) for users, which have not been well jointly addressed. To fill this gap, this paper formulates a load balance problem with the goal of minimizing the maximum load among PVs, under the constraints of service reliability and QoS for tasks. The NP-hardness of the problem is proved. Due to the NP-hardness and coupled variables, the problem is decomposed into two subproblems, i.e., task offloading problem and resource allocation problem. An efficient algorithm is proposed to iteratively solve the two subproblems. For the first subproblem, this paper derives a lower bound of the allocated computing resources for each task, and designs a greedy algorithm to make offloading decision based on the derived bound. For the second subproblem, this paper proves its convexity and proposes a subgradient algorithm to make resource allocation. The service reliability of PVs is evaluated from the traces in a real-world dataset. Experimental results show that, the proposed algorithm can effectively reduce the maximum load among PVs, compared with the existing algorithms.
code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1–2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry.
