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Dual Attention-Based Federated Learning for Wireless Traffic Prediction

Chuanting Zhang, Shuping Dang, Basem Shihada, Mohamed-Slim Alouini

arXiv:2110.05183v1cs.DC

TL;DR

Centralized wireless traffic prediction transfers large amounts of raw data, while federated prediction must contend with heterogeneous traffic patterns. FedDA combines augmentation-assisted clustering, a quasi-global model, and dual-attention aggregation; it outperforms baseline methods across traffic types and two datasets, including with only 1% of augmentation data shared.

  • Problem

    Centralized wireless traffic prediction transfers large amounts of raw data, and federated prediction faces mobility-related spatiotemporal coupling and heterogeneous traffic across BSs.

    Source: §I · p. 1

  • Method

    FedDA clusters BSs using augmented traffic and location, shares a quasi-global model as prior knowledge, and uses dual attention and hierarchical aggregation.

    Source: §I · p. 2

  • Results

    FedDA outperforms baseline methods for all traffic types in both datasets, even when only 1% of augmentation data is shared.

    Source: C. Experimental Settings and Overall… · p. 7

  • Takeaways & Limitations

    The aggregation design aims to balance generality and specialty while capturing cluster-specific patterns and retaining global-model transferability.

    Source: §I · p. 2

Abstract

from arXiv · show

Wireless traffic prediction is essential for cellular networks to realize intelligent network operations, such as load-aware resource management and predictive control. Existing prediction approaches usually adopt centralized training architectures and require the transferring of huge amounts of traffic data, which may raise delay and privacy concerns for certain scenarios. In this work, we propose a novel wireless traffic prediction framework named \textit{Dual Attention-Based Federated Learning} (FedDA), by which a high-quality prediction model is trained collaboratively by multiple edge clients. To simultaneously capture the various wireless traffic patterns and keep raw data locally, FedDA first groups the clients into different clusters by using a small augmentation dataset. Then, a quasi-global model is trained and shared among clients as prior knowledge, aiming to solve the statistical heterogeneity challenge confronted with federated learning. To construct the global model, a dual attention scheme is further proposed by aggregating the intra- and inter-cluster models, instead of simply averaging the weights of local models. We conduct extensive experiments on two real-world wireless traffic datasets and results show that FedDA outperforms state-of-the-art methods. The average mean squared error performance gains on the two datasets are up to 10\% and 30\%, respectively.

I. INTRODUCTION

Wireless traffic prediction supports network management, but centralized training transfers large amounts of raw data, while federated prediction must address mobility-related spatiotemporal coupling and heterogeneous BS traffic. FedDA combines augmentation-assisted clustering, a quasi-global model, and dual attention with hierarchical aggregation to train collaboratively across BSs.

I. INTRODUCTION

  • Mobility-driven spatiotemporal coupling and distinct BS traffic patterns make network-wide prediction challenging under federated learning.Centralized approaches also transfer large amounts of raw data, potentially consuming network capacity and affecting payload transmissions.Source: §I · pp. 1–2
  • FedDA groups BSs using augmented traffic patterns and geographic locations, then uses a quasi-global model to mitigate generalization difficulty from statistical heterogeneity.The clustering strategy is described as capturing different traffic patterns while protecting data privacy.Source: §I · p. 2
  • Dual attention-based aggregation and a hierarchical structure combine intra- and inter-cluster models to capture cluster-specific patterns while supporting global-model transferability.The framework aims to balance generality and specialty rather than simply averaging local-model weights.Source: §I · p. 2
  • The paper evaluates FedDA on two real-world datasets and compares its experimental results with existing algorithms.The authors state that they test the framework's effectiveness and efficiency.Source: §I · p. 2

II. RELATED WORK

The related-work section surveys wireless traffic prediction and federated learning, positioning FedDA as a federated approach to heterogeneous wireless traffic prediction.

II. RELATED WORK

  • The related-work section reviews prior work on wireless traffic prediction and federated learning.It frames these as the two research topics most closely related to the paper.Source: §II · p. 2

A. Wireless Traffic Prediction

  • Wireless traffic prediction work ranges from simple historical-average and naïve methods to statistical models such as ARIMA and deep-learning approaches.The section notes that simple methods can miss hidden traffic patterns and describes deep models for spatial and temporal dependencies.Source: A. Wireless Traffic Prediction · pp. 2–3

B. Federated Learning

  • FedAvg can degrade on non-IID client data, while prior FL approaches address heterogeneity through data sharing [29] or FedProx, and FedAtt weights client contributions attentively.The paper distinguishes its focus as federated wireless traffic prediction with highly heterogeneous data.Source: B. Federated Learning · p. 3

III. PROBLEM FORMULATION AND PRELIMINARIES

  • The next section formulates wireless traffic prediction and describes the FL implementation for that problem.Source: §III · p. 3

A. Problem Formulation

The formulation treats wireless traffic prediction as estimating a future traffic volume from current and historical traffic. It uses sliding-window input-output pairs and minimizes prediction loss across BSs.

A. Problem Formulation

  • The prediction target is a future traffic volume estimated from current and previous traffic volumes at each BS.The model may be linear or nonlinear, with f(·) denoting the prediction model and w its parameters.Source: A. Problem Formulation · p. 3
  • Sliding-window inputs use selected historical traffic to form input-output pairs, with p and q capturing closeness and periodic dependence for one-step-ahead prediction.The passage defines φ as the periodicity.Source: A. Problem Formulation · p. 3
  • The objective is to minimize prediction error over all K BSs using a loss function L.Source: A. Problem Formulation · p. 3

B. Preliminaries of Federated Learning

Federated learning sends a global model to base stations for local updates and aggregates their models at a central server. FedDA extends this process with augmentation-assisted clustering and dual-attention federated optimization.

B. Preliminaries of Federated Learning

  • At each round, the server sends the global model to base stations, which update it using local data and return their local models for aggregation.The local update uses the learning rate η and the gradient of the loss with respect to the model parameters.Source: B. Preliminaries of Federated Learning · p. 3
  • The classical federated averaging scheme aggregates local models at the server, and repeated rounds yield a final global model.Source: B. Preliminaries of Federated Learning · p. 4

IV. PROPOSED FRAMEWORK

  • FedDA first sends augmented data to the server for clustering and quasi-global model construction, then coordinates base stations to train a global model using dual-attention optimization.The framework description identifies these as its three steps.Source: §IV · p. 4

A. Wireless Traffic Data Augmentation

FedDA creates a small augmentation dataset from weekly traffic averages and uses augmented traffic patterns alongside base-station locations to cluster clients. The augmented data also supports a quasi-global model used as prior knowledge.

A. Wireless Traffic Data Augmentation

  • Wireless traffic differs across base stations because of urban functions and user mobility, making the data heterogeneous and non-IID.The paper presents a small augmentation dataset as its strategy for addressing statistical heterogeneity.Source: A. Wireless Traffic Data Augmentation · p. 4
  • Weekly traffic slices are averaged at each time point, then standardized to zero mean and unit variance to form augmented data.The passage says Fig. 2 illustrates the procedure and compares augmented with original traffic.Source: A. Wireless Traffic Data Augmentation · p. 4, Figure 2 · p. 4
  • 0.9526 is the Pearson correlation coefficient between augmented and original traffic, indicating high similarity.Source: A. Wireless Traffic Data Augmentation · p. 4
  • Each base station sends a very small share of augmented data to the server, where a dataset with a uniform distribution over y supports quasi-global model training.The quasi-global model uses augmented rather than original data, has the same architecture as local and global models, and serves as prior knowledge because the augmented data resembles the original.Source: A. Wireless Traffic Data Augmentation · p. 4

B. Iterative Clustering for BSs

1 Random initialize {vc}C

The clustering procedure iterates between traffic-pattern and geographic clustering, using geographic cluster centers to initialize traffic clustering when the two assignments differ.

1 Random initialize {vc}C

  • If traffic-pattern and geographic clustering produce different assignments, geographic cluster centers initialize another K-Means run on traffic data; iteration stops when the results agree.The procedure uses geographic information in traffic-pattern clustering and returns a cluster label for each base station.Source: §1 · p. 5
  • After clustering, each base station has a cluster label, and FedDA proceeds to federated optimization.Source: §1 · p. 5

C. Dual Attention-Based Model Aggregation

FedDA uses layer-wise dual attention and hierarchical aggregation to combine local and quasi-global models. Intra-cluster aggregation forms cluster models, and inter-cluster aggregation produces a global model incorporating knowledge across clusters.

C. Dual Attention-Based Model Aggregation

  • FedDA aggregates models first within clusters to capture cluster-specific traffic patterns, then across clusters to generate a global model incorporating their knowledge.The scheme applies attention layer by layer to quantify contributions from local and quasi-global models.Source: C. Dual Attention-Based Model… · p. 5
  • The global model is optimized to balance unique and shared traffic patterns by minimizing weighted distances to local and quasi-global models.The weights are self-adaptive attention scores; αm and β denote layer-wise contributions, while ρ is a task-dependent regularization parameter.Source: C. Dual Attention-Based Model… · p. 5
  • The server updates the output model by gradient descent, with γ controlling the step size in the opposite-gradient direction.Source: C. Dual Attention-Based Model… · p. 6

V. EXPERIMENTS

The experiments assess FedDA’s effectiveness and efficiency against existing algorithms. They cover datasets, metrics, baselines, experimental settings, overall prediction performance, and the influence of learning parameters.

V. EXPERIMENTS

  • The experiments compare FedDA with existing algorithms to evaluate its effectiveness and efficiency.The paper also examines overall prediction performance and the influence of learning parameters.Source: §V · p. 6
  • The evaluation introduces the datasets, metrics, and baseline methods before specifying experimental settings such as learning rate and batch size.The passage does not specify the datasets, metrics, or baselines themselves.Source: §V · p. 6
  • The reported results cover overall prediction performance and the influence of learning parameters.Source: §V · p. 6

A. Dataset and Evaluation Metrics

The evaluation uses hourly wireless-traffic data from two Italian areas and compares FedDA with five baselines, including distributed predictors and federated methods.

A. Dataset and Evaluation Metrics

  • Traffic data come from BS-served cells in Milan and Trentino, with SMS, voice-call, and Internet activity recorded every ten minutes over two months and resampled hourly.Milan contains 10,000 grid cells and Trentino 6,575; the datasets are publicly available.Source: A. Dataset and Evaluation Metrics · p. 6
  • Prediction performance is evaluated using mean squared error (MSE) and mean absolute error (MAE).Source: A. Dataset and Evaluation Metrics · p. 6

B. Baseline Methods

  • The five baselines are Lasso, SVR, LSTM, FedAvg, and FedAtt; the first three train per client, while FedAvg, FedAtt, and FedDA use federated training.FedAvg averages local weights, whereas FedAtt differentiates client-model contributions during aggregation.Source: B. Baseline Methods · p. 6

C. Experimental Settings and Overall Results

Across both datasets and all three traffic types, FedDA outperforms the baselines, including with only 1% of augmentation data shared. Its reported advantages include lower MSE and MAE, and comparisons also examine prediction errors and model behavior.

C. Experimental Settings and Overall Results

D. Communication Rounds Versus Prediction Accuracy

FedDA reaches higher prediction accuracy with fewer communication rounds, while its performance also depends on the regularizer ρ and cluster size C. Experiments report that clustering usually lowers prediction errors, and the conclusion describes a hierarchical dual-attention approach for combining models.

D. Communication Rounds Versus Prediction Accuracy

  • After 30 communication rounds, FedDA’s R-squared scores were 0.74, 0.89, and 0.86 for Milano SMS, Call, and Internet, versus FedAtt’s 0.69, 0.84, and 0.83.The authors also report that FedDA needs fewer communications to reach a given accuracy on both datasets.Source: D. Communication Rounds Versus… · p. 8

E. Effect of ρ on Prediction Performance

F. Effect of C on Prediction Performance

VI. CONCLUSION

  • FedDA combines traffic-pattern and location-based BS clustering, a shared quasi-global model, and dual-attention aggregation in a hierarchical architecture.The authors evaluate its effectiveness and efficiency on two real-world datasets.Source: §VI · p. 9

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