AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Experiments under representative Non-IID settings on benchmark datasets show that PFLS-One achieves improved accuracy and faster convergence compared with representative baseline methods, and the convergence analysis under a non-convex objective provides theoretical support for the proposed method.
A prototype space optimization mechanism based on NT‐Xent that alleviates insufficient prototype discriminability by promoting intra‐cluster aggregation and increasing intercluster distance is devised and an adaptive model decoupling strategy is introduced to address the remaining intra‐cluster heterogeneity.
In multi-domain networking, virtual network function (VNF) scaling using machine learning requires an accurate prediction model while addressing privacy constraints and non-identical and independently distributed (non-IID) data across domains. Current models have used conventional federated learning (FL) methods, such...
Federated Learning (FL) enables collaborative model training without sharing raw data, but its effectiveness degrades under Non-IID client data and inefficient participation. In such settings, FedAvg may exhibit slow convergence, high variance across rounds, and elevated communication overhead. We propose a clustered e...
Prototype-based knowledge sharing effectively mitigates data and model heterogeneity in federated learning (FL) by exchanging class-level semantic information. However, existing methods typically assume all local prototypes are equally reliable. Consequently, low-quality prototypes from heterogeneous models or dynamic...
Federated learning (FL) collaboratively trains models across networked industrial Internet of Things (IIoT) terminals. However, statistical heterogeneity in IIoT data often hinders the performance of global models. Current FL methods typically focus on single-level representation alignment and fail to exploit gradient-...
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, but its performance degrades significantly under heterogeneous data distributions. While Clustered Federated Learning (CFL) mitigates heterogeneity by grouping similar clients, most existing methods primari...
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