Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients’ constraints and balanced model performance across various scenarios. In this survey, we delineate a comprehensive classification of the state-of-the-art fairness-aware approaches from a multifaceted perspective, i.e., model performance-oriented and capability-oriented. Moreover, we provide a framework to categorize and address various fairness concerns and associated technical aspects, examining their effectiveness in balancing equity and performance within FL frameworks. We further examine several significant evaluation metrics leveraged to measure fairness quantitatively. Finally, we explore exciting open research directions and propose prospective solutions that could drive future advancements in this important area, laying a solid foundation for researchers working toward fairness in FL.

Modeling the interactions among agents based on their historical trajectories is key to precise multi-agent trajectory prediction. Hypergraph Convolutional Networks (HGCN) have become a proper choice for capturing high-order interactions among agents in this field. However, most existing works only consider static hypergraphs, and ignore that in a hypergraph, the power of influence varies between vertices (or hyperedges). Therefore, we propose DyHGDAT, a dynamic hypergraph dual attention network to capture the high-order interactions among agents, which not only models the evolution of hypergraph over time but also highlights the vertices and hyperedges with larger impacts. We apply DyHGDAT to a CVAE-based prediction system for predicting plausible trajectories. To validate the effectiveness of prediction, we evaluate our proposed method on two well-established trajectory prediction datasets: the ETH/UCY datasets and the Stanford Drone Dataset (SDD). The experimental results show that with DyHGDAT, the CVAE-based prediction system out-performs state-of-the-art methods by 12.5%/5.3% in ADE/FDE on ETH/UCY, and the improvement on SDD is 6.4%/7.4%.

OBJECTIVE: Predicting breast cancer survival and targeting patients at high-risk of mortality is of crucial importance. METHODS: We built a Bayesian Dynamic Cox (BDCox) model for predicting 5-year overall survival in breast cancer patients using data of the SEER Cancer Registry with 12,840 women. Four feature selection methods were used to identify predictors and enhance parsimony: fast backward variable selection, elastic net, Bayesian Model Average (BMA), and clinical expertise. All resulting models and a baseline full model containing all features were internally validated via bootstrapping and externally validated in the Shanghai Breast Cancer Survival Study. RESULTS: BMA outperformed other feature selection methods in both internal and external validations. The BDCox model with 12 predictors had the best performance. Several predictors showed time-varying associations with survival that are in agreement with previous studies. CONCLUSION: The model developed using BDCox outperformed other prognostic models considered in our study. The internal validation results indicate that the BDCox model is capable of achieving high prediction accuracy (C-statistic: 0.802), and the external validation results showed excellent generalizability of the BDCox model (C-statistic: 0.739). SIGNIFICANCE: We built a dynamic Bayesian model from the large population-based registry SEER for predicting 5-year breast cancer overall survival. The prediction performance of the BDCox model is significantly better than other survival models.

Soft computing is facing a rapid evolution thanks to the development of artificial intelligence especially the deep learning. With video surveillance technologies of soft computing, such as image processing, computer vision, and pattern recognition combined with cloud computing, the construction of smart cities could be maintained and greatly enhanced. In this article, we focus on the online detection of action start task in video understanding and analysis, which is critical to the multimedia security in smart cities. We propose a novel model to tackle this problem and achieves state-of-the-art results on the benchmark THUMOS14 data set.

A fundamental issue of 6G networks with aerial access networks (AAN) as a core component is that user devices will send high‐volume traffic via AAN to backend servers. As such, it is critical to load balance such traffic such that it will not cause network congestion or disruption and affect users’ experience in 6G networks. Motivated by the success of software‐defined networking‐based load balancing, this paper proposes a novel system called Tigris, to load balance high‐volume AAN traffic in 6G networks. Different from existing load balancing solutions in traditional networks, Tigris tackles the fundamental disruption-resistant challenge in 6G networks for avoiding disruption of continuing flows and the control-path update challenge for limiting the throughput of updating load balancing instructions. Tigris achieves disruption‐free and low‐control‐path‐cost load balancing for AAN traffic by developing an online algorithm to compute disruption‐resistant, per‐flow load balancing policies and a novel bottom‐up algorithm to compile the per‐flow policies into a highly compact rule set, which remains disruption‐resistant and has a low control‐path cost. We use extensive evaluation to demonstrate the efficiency and efficacy of Tigris to achieve zero disruption of continuing AAN flows and an extremely low control‐path update overhead, while existing load balancing techniques in traditional networks such as ECMP cause high load variance and disrupt almost 100% continuing AAN flows.

OBJECTIVE: We evaluated the prognostic value of lymph node ratio (LNR) for the survival of breast cancer patients using Bayesian inference. METHODS: Data on 5,279 women with infiltrating duct and lobular carcinoma breast cancer, diagnosed from 2006-2010, was obtained from the NCI SEER Cancer Registry. A prognostic modeling framework was proposed using Bayesian inference to estimate the impact of LNR in breast cancer survival. Based on the proposed model, we then developed a web application for estimating LNR and predicting overall survival. RESULTS: The final survival model with LNR outperformed the other models considered (C-statistic 0.71). Compared to directly measured LNR, estimated LNR slightly increased the accuracy of the prognostic model. Model diagnostics and predictive performance confirmed the effectiveness of Bayesian modeling and the prognostic value of the LNR in predicting breast cancer survival. CONCLUSION: The estimated LNR was found to have a significant predictive value for the overall survival of breast cancer patients. SIGNIFICANCE: We used Bayesian inference to estimate LNR which was then used to predict overall survival. The models were developed from a large population-based cancer registry. We also built a user-friendly web application for individual patient survival prognosis. The diagnostic value of the LNR and the effectiveness of the proposed model were evaluated by comparisons with existing prediction models.

Abstract The key impediments to a successful wireless sensor network (WSN) application are the energy and the longevity constraints of sensor nodes. Therefore, two signal processing oriented cluster management strategies, the proactive and the reactive cluster management, are proposed to efficiently deal with these constraints. The former strategy is designed for heterogeneous WSNs, where sensors are organized in a static clustering architecture. A non‐myopic cluster activation rule is realized to reduce the number of hand‐off operations between clusters, while maintaining desired estimation accuracy. The proactive strategy minimizes the hardware expenditure and the total energy consumption. On the other hand, the main concern of the reactive strategy is to maximize the network longevity of homogeneous WSNs. A Dijkstra‐like algorithm is proposed to dynamically form active cluster based on the relation between the predictive target distribution and the candidate sensors, considering both the energy efficiency and the data relevance. By evenly distributing the energy expenditure over the whole network, the objective of maximizing the network longevity is achieved. The simulations evaluate and compare the two proposed strategies in terms of tracking accuracy, energy consumption and execution time. Copyright © 2010 John Wiley & Sons, Ltd.

The prime motivation of our work is to balance the inherent trade-off between the resource consumption and the accuracy of the target tracking in wireless sensor networks. Toward this objective, the study goes through three phases. First, a cluster-based scheme is exploited. At every sampling instant, only one cluster of sensors that located in the proximity of the target is activated, whereas the other sensors are inactive. To activate the most appropriate cluster, we propose a nonmyopic rule, which is based on not only the target state prediction but also its future tendency. Second, the variational filtering algorithm is capable of precise tracking even in the highly nonlinear case. Furthermore, since the measurement incorporation and the approximation of the filtering distribution are jointly performed by variational calculus, an effective and lossless compression is achieved. The intercluster information exchange is thus reduced to one single Gaussian statistic, dramatically cutting down the resource consumption. Third, a binary proximity observation model is employed by the activated slave sensors to reduce the energy consumption and to minimize the intracluster communication. Finally, the effectiveness of the proposed approach is evaluated and compared with the state-of-the-art algorithms in terms of tracking accuracy, internode communication, and computation complexity.

Resource limitations in wireless sensor networks have put stringent constraints on distributed signal processing. In this paper, we propose a cluster-based decentralized variational filtering algorithm with minimum resource allocation for simultaneous sensor localization and target tracking. At each sampling instant, only one cluster of sensors is activated according to the prediction of the target state. Slave sensors employ a binary proximity observation model to reduce energy consumption and minimize communication cost. Based on the binary measurements between sensors and the target, activated sensors and target location estimates are interdependently improved. By adopting the variational method, the inter-cluster information exchange is reduced to one single Gaussian statistic, further minimizing resource consumption in the network. Since the measurement incorporation and the approximation of the filtering distribution are jointly performed by variational calculus, an effective and lossless compression is achieved compared to the classical particle filtering. Effectiveness of the proposed approach is evaluated in terms of tracking accuracy and localization precision.

A fundamental assumption underlying most studies of optical burst switched (OBS) networks is that full wavelength conversion is available throughout the network. In practice, however, economic and technical considerations are likely to dictate a more limited and sparse deployment of wavelength converters in the optical network. Therefore, we expect wavelength assignment policies to be an important component of OBS networks. In this paper, we explain why wavelength selection schemes developed for wavelength routed (circuit-switched) networks are not appropriate for OBS. We then develop a suite of adaptive and nonadaptive policies for OBS switches. We also apply traffic engineering techniques to reduce wavelength contention through traffic isolation. Our performance study indicates that, in the absence of full conversion capabilities, intelligent choices in assigning wavelengths to bursts at the source can have a profound effect on the burst drop probability in an OBS network.

A fundamental assumption underlying most studies of optical burst switched (OBS) networks is that full wavelength conversion is available throughout the network. In practice however, economic and technical considerations are likely to dictate a more limited and sparse deployment of wavelength converters in the optical network. Therefore, we expect wavelength assignment policies to be an important component of OBS networks. In this paper, we explain why wavelength selection schemes developed for wavelength routed networks are not appropriate for OBS. We then develop a suite of adaptive and non-adaptive policies for OBS switches. We also apply traffic engineering techniques to reduce wavelength contention through traffic isolation. Our performance study indicates that, in the absence of full conversion capabilities, intelligent choices in assigning wavelengths to bursts at the source can have a profound effect on the burst drop probability in an OBS network.

This paper studies the important fault management issue with focus on the fast restoration mechanisms for optical burst switched (OBS) networks. In order to reduce the burst losses during the restoration process, effective fast restoration schemes are necessary. This is illustrated via two basic fast restoration schemes, the distributed deflection scheme and the local deflection scheme, compared with the slow global routing update mechanism. A novel priority-based QoS restoration scheme is also proposed to provide differentiated restoration services. Through detailed descriptive analysis and a comprehensive simulation study, these fast restoration schemes demonstrate fast restoration process, low fault management overheads, and excellent burst loss performance. As far as we know, this is the first comprehensive study on the restoration mechanisms for OBS networks.