Federated learning (FL) faces challenges in ensuring personalized model performance while maintaining strong privacy protection. Combined with knowledge distillation, federated learning enhanced by differential privacy has garnered increased attention. However, existing privacy-preserving federated distillation methods often apply a uniform privacy budget, neglecting the varying privacy needs across heterogeneous clients. In this letter, we propose PDP-FedKD, aPersonalizedDifferentialPrivacy-basedFederatedKnowledgeDistillationapproach. PDP-FedKD allows clients to self-select their privacy budgets using a nonuniform sampling strategy and introduces tailored noise into distillation models across heterogeneous clients. Our approach ensures global model generalization and local model personalization while preserving the global privacy budget. We further analyze tight privacy accounting based on Rényi differential privacy to optimize the trade-off between privacy and model accuracy. Experimental results in non-IID settings show that PDP-FedKD improves model performance by 4.06% over LDP-FedAKD with a uniform privacy budget and by 7.73% over DP-FedAvg without optimizing heterogeneity.

Recently, reconstruction-based deep models have gained widespread usage in unsupervised anomaly detection. However, they may overlook some anomalies owing to the over-generalization of neural networks. Several studies have incorporated memory networks to mitigate this problem. Nonetheless, some of them lack an explicit memory updating process, while others rely on data-driven updating methods that are sensitive to initial values and unsuitable for end-to-end training. Additionally, the traditional criterion for detection computed in the high-dimensional input space may collapse as the spike in the deviation score is averaged across numerous dimensions. To address these challenges, we propose MCAKE, a M emory-augmented C ontrastive A utoencoder with K NN-Based E xtraction. It is designed to highlight the deviation score for anomalies by reconstructing input using fixed normal prototypes recorded in the memory. We explicitly encourage the memory to be autonomously learned and effectively allocated through contrastive learning with multiple positive and multiple negative samples. Furthermore, we introduce a bivariate detection criterion that calculates anomaly scores considering both input and latent space to tackle the collapse. Extensive experiments on 50 datasets across various categories demonstrate the superiority of our approach, with a 2% relative improvement over the previous state-of-the-art models.

As media streaming grows, service providers have to focus more on users’ quality of experience (QoE). Among the metrics that influence QoE, network-oriented and media-oriented factors have been the primary concern of most existing work. Apart from the two factors, however, another crucial aspect of QoE isthe user. Though the analysis and modeling of users is a turning point in the transition from QoS to QoE, the impact of users’ memory and cognitive-psychological effects have not been fully explored. In this work, we analyze, validate and quantify the relationship between multiple cognitive effects and the QoE of users in media streaming. We propose PsyQoE, a new QoE assessment framework that uniquely focuses on user perceptions during media sessions. Besides providing real-time QoE assessments through leveraging machine learning, PsyQoE also predicts the overall QoE by taking into account user biases and long-term effects. Additionally, we have designed a further modified version to cater to the needs of hardware and energy-restricted scenarios. When compared with other existing methods, our approach demonstrates enhanced performance by boosting accuracy by 7% to 16%. It also provides better interpretability and broader applicability.

Fixed broadband internet service can provide a stable broadband network of up to 100 megabits or even gigabits and users at home can use fixed broadband service for all kinds of internet surfing, including website and application access, watching videos, playing games, etc. Traditional maintenance for fixed broadband networks primarily uses human manual meth-ods, supplemented by some low-level semi-automation operations. Since the long processes with numerous network elements in the fixed broadband network, it is difficult for traditional operation and maintenance to support effectively with high quality. When abnormalities occur, it is quite manpower cost and time cost to monitor and locate faults. Therefore, to improve the autonomous capability of the fixed broadband network, intelligent operation and maintenance methods are necessary. First of all, a brand-new data pre-process method is proposed to detect anomalies and problems of slow access by selecting web services access commonly visited by users. Secondly, as the fixed broadband network is a multi-level and complex structure with only a small amount of anomaly sample data, we propose a multi-source joint anomaly detection model called MSJAD model on multi-dimensional features data. The model validation results on real datasets from the real fixed broadband network are state-of-the-art. The accuracy rate reaches 98 % and the recall is over 99 %. We have already begun to deploy the model on the real fixed broadband network and have achieved good feedback.

Flow classification in an IP switch is discussed. Different traditional flow classifiers are briefly introduced. Then a Hopfield neural network flow classifier (HNNC) is proposed. It can detect services as suitable for switching in accordance with the virtual circuit space of the IP switch. The simulation results show that services suitable to be switched are detected by HNNC and the majority of packets are assigned to a minimum number of flows.