A traffic-aware federated learning prediction framework with custom aggregation
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Bibliographic record
Abstract
Reliable traffic predictions are essential for managing congestion, optimizing routes, improving commuter safety, and advancing the performance of intelligent transportation systems (ITS). However, existing centralized systems often lack adaptability to real-world traffic patterns and fail to capture spatio-temporal variability and client-level heterogeneity. These systems require large amounts of sensitive data to be collected on central servers, intensifying privacy risks. This study proposes a privacy-preserving Federated Learning (FL) framework for traffic flow and speed prediction (5 to 60 mins ahead) using non-independent and identically distributed (non-IID) traffic data. The objectives of this study are threefold: (1) design a client-aware custom FL aggregation strategy that accounts for traffic heterogeneity and client-specific dynamics, ignored in standard FL methods, (2) improve personalization by grouping clients based on real-world traffic pattern similarity via clustering-based approach and, (3) enhance convergence and predictive performance of global aggregation using dynamic, traffic-aware aggregation scores. The proposed framework designs a hybrid FL long-short-term memory (FedLSTM) model augmented with an attention mechanism to effectively model both temporal and spatial traffic variations across junctions, while ensuring that all raw data remains local. To improve learning under traffic diversity and imbalanced traffic distribution patterns, we propose a custom traffic-aware aggregation strategy that dynamically weighs client contributions based on six traffic-based metrics. Evaluations on clustered client partitions demonstrate that our custom aggregation consistently outperformed the baseline strategies across multiple evaluation metrics. These results highlight the effectiveness of integrating traffic-aware aggregation in enhancing the performance and generalization capability of FL-based traffic prediction frameworks. • Privacy-preserving FL framework for traffic prediction under non-IID conditions. • Novel traffic-aware custom FL aggregation using six traffic metrics. • Traffic-informed clustering for client partitioning to simulate a cross-silo setup. • Hybrid attention-based FedLSTM for multi-output traffic prediction across regions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it