This study proposes the FedU method, which applies uniform aggregation weights to all clients and uses the total number of active clients as a coefficient weight and achieves better CNN model performance than the three baseline aggregation methods across the evaluation metrics of loss, MAE, and $R^{2}$ .
Abstract
One of the main challenges in implementing federated learning (FL) is the non-independent and identically distributed (non-IID) nature of inter-client data, which can affect server aggregation. When using a basic aggregation method such as FedAvg, the aggregation result is influenced by the client with the largest number of samples, potentially leading the resulting global model to fail to reflect the data characteristics of most clients. This study proposes the FedU method, which applies uniform aggregation weights to all clients and uses the total number of active clients as a coefficient weight. The CNN model was used to predict downlink throughput using a cellular network key performance indicator (KPI) traffic dataset collected from six base stations (BSs) within the same cluster, located in the urban area of Bandung, Indonesia. The performance of FedU was compared with three baseline methods, namely FedAvg, FedProx, and FedLBW. The experimental results show that the CNN model’s performance is no longer influenced by the number of samples, but is instead determined by the sample values and the dataset’s distribution. FedU achieves better CNN model performance than the three baseline aggregation methods across the evaluation metrics of loss, MAE, and $R^{2}$ .
Federated learning (FL) enables cellular traffic prediction without centralizing raw base-station data, but statistical heterogeneity makes a single global model unsuitable for many clients. This paper proposes federated clustering with adaptive personalization (FedCAP), a parameter-efficient personalized FL framework that separates cluster-level representation learning from client-level adaptation. Clients are grouped using training-only daily traffic profiles, after which an LSTM backbone is trained by FedAvg within each cluster. Each client then freezes the cluster backbone and optimizes a residual bottleneck adapter locally. The adapter contains 4241 trainable parameters, 6.19% of the 68,483-parameter three-feature backbone and prediction head, and personalization transmits no model updates. In a shared-seed-42 comparison across 11 methods and four public datasets, FedCAP ranks first or second in 12 of 16 dataset–metric combinations. Across five shared seeds, its mean MAE is 6.66%, 7.47%, 2.64%, and 3.86% below FedAvg on the Milan, Trentino, Bihar, and Taiwan datasets, respectively. Holm-adjusted paired t-tests identify 6 significant dataset–metric differences, whereas exact Wilcoxon tests are not significant because each comparison contains only five nonzero seed-matched pairs; the statistical evidence is therefore interpreted conservatively.
Xingyu Tian, Citong Que, Faisal Nadeem Khan· Telecom· 0 citations
FedTraffic, a hierarchical federated learning framework for traffic flow forecasting that integrates Edge–Fog–Cloud computing, hybrid deep learning, adaptive federated optimization, and Explainable Artificial Intelligence, is proposed.
This paper presents FedGAT, a federated graph attention network for joint user association and inter-remote radio head (RRH) interference mitigation in cloud radio access networks (C-RAN). Centralized optimization requires global channel state information (CSI) at the baseband unit (BBU) pool, which adds fronthaul overhead and exposes user data. Existing federated learning schemes use flat local models that do not represent the interference graph, which lowers performance in heterogeneous multi-cell settings. In FedGAT, each RRH builds a local interference graph from its own CSI and trains a GATv2 model by local Adam gradient steps. Only the parameter increments are sent to the BBU pool, which combines them by weighted FedAvg without exchanging raw CSI. We formulate the joint user association and power allocation problem as a mixed-integer non-convex program and derive a graph neural network (GNN)-based continuous relaxation suitable for distributed training. A non-asymptotic convergence bound is obtained for non-identically distributed local interference graphs, showing that the optimality gap grows with the local step count and a measure of graph heterogeneity. Under the 3GPP Urban Macrocell channel model, FedGAT increases the steady-state weighted sum-rate by 18.7% over a federated multilayer perceptron baseline and by 49.2% over a centralized Graph Attention Network version 2 (GATv2) model at equal training budgets, while keeping raw CSI local. These margins are averaged over ten Monte Carlo channel realizations and reported with their standard deviations, and the fronthaul benefit of FedGAT refers to privacy and CSI-free inference after training rather than training-phase traffic.
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.
Baobao Chai, Zhongyuan Yu, Tianqing He et al.· 0 citations