Federated learning for heterogeneous edge environments_ A multi-domain evaluation on object detection and energy forecasting

GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT

Visual object detection is essential for defect inspection and process monitoring in edge-deployed Industrial Internet of Things (IIoT). Yet, training accurate detectors across distributed factories faces stringent constraints on data privacy, annotation budgets, and uplink communication. Standard federated learning (FL) preserves locality but often wastes labeling resources on redundant frames and overlooks detection-specific gradient alignment when scheduling clients. To bridge this gap, we propose Gradient-Aligned Active Federated Object Detection (GA-AFedOD), a unified framework that jointly optimizes annotation selection, client participation, and model aggregation as a constrained stochastic program. A novel utility metric integrates box-level uncertainty, prototype diversity, gradient alignment, and resource pricing, enabling edge clients to perform locally guided active querying while the server solves a lightweight primal-dual problem for budget-aware client scheduling. We prove a submodular approximation guarantee for the greedy sampling rule and establish a non-convex convergence bound that explicitly captures the impact of label budgets, client drift, and compression noise. This article further clarifies the relationship with recent federated active learning and industrial detection studies, adds parameter and theory-diagnostic analyses, and distinguishes controlled simulation evidence from real-world deployment validation on industrial datasets such as RasPiDets, Electric Power Fitting Dataset (EPFD), and Diverse Insulator Dataset (DINS). Controlled simulation results show that GA-AFedOD achieves considerably higher mean average precision (mAP) while reducing both annotation costs and uplink consumption by over 40% compared with competitive baselines.

Metadata Clustering-Driven Federated Learning for Multi-Domain Virtual Network Function Scaling

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 as federated averaging (FedAvg), yet they suffer from degraded performance due to heterogeneous traffic patterns in multi-domain networks. However, existing studies have not addressed the impact of non-IID characteristics on FL-based VNF scaling or developed an effective solution to mitigate it. This paper proposes a metadata-clustering-driven FL method that clusters domains with different traffic patterns and trains cluster-specific models. We extract statistical, spectral, and temporal features to represent traffic disturbance. We apply principal component analysis (PCA) followed by K-means clustering to group time series. We apply FedAvg within clusters to train cluster-specific prediction models. To evaluate the performance of the proposed method, we set up a testbench to synchronize three non-IID patterns. The numerical results demonstrate that the proposed clustered FL method consistently achieves a lower mean squared error (MSE) than the FedAvg baseline across all four evaluated non-IID settings. The proposed method yields an MSE of 0.7056 (a 23.1% reduction from FedAvg’s 0.9176) under label skew, 0.3615 (a 4.6% reduction from 0.3790) under label and feature skew, 0.6958 (a 28.1% reduction from 0.9682) under label and quantity skew, and 0.3748 (a 0.7% reduction from 0.3774) under the combined skew setting. These consistent reductions in MSE demonstrate that the proposed method effectively mitigates the performance degradation typically caused by non-IID effects.

FedLEAF: Bi-directional Prototype Adaptation for Heterogeneous Federated Learning

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 clients can contaminate the global aggregation, leading to a vicious cycle of noise accumulation and performance degradation. To address this, we propose FedLEAF, a Federated Learning framework with server-side proactive Evaluation and clientside Adaptive Fusion. Specifically, the server employs an Adaptive Learning Prototypes (ALP) network to dynamically evaluate prototype reliability and generate learnable aggregation weights, ensuring that highquality prototypes exert a primary influence on the global model. Meanwhile, the client utilizes a Historical Consistency Fusion (HCF) strategy to selectively absorb global knowledge by assessing its consistency with locally maintained historical prototypes. Extensive experiments on standard datasets demonstrate that FedLEAF achieves effective improvements in model accuracy and robustness compared to existing methods.

Federated Learning Approach for Multi-Regional Traffic Flow Prediction

Experiments on controlled synthetic data and the Q-Traffic real-world dataset demonstrate that the proposed framework improves predictive accuracy, cross-client stability, and robustness under heterogeneous federated traffic scenarios.

Parameter-Efficient Personalized Federated Learning for Accurate Cellular Traffic Prediction

FedTP: A Federated Learning Framework for Traffic Prediction

FedTP is proposed, a federated learning framework that integrates gradient conflict elimination into the aggregation process and harmonizes local updates, thereby improving fairness across clients without compromising overall predictive accuracy.