AdaFML: Adaptive Federated Meta Learning With Multi-Objectives and Context-Awareness in Dynamic Heterogeneous Networks
Why this work is in the frame
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Bibliographic record
Abstract
Recent advancements in Federated Learning (FL) have enabled the widespread deployment of distributed computing resources across connected devices, enhancing data processing capabilities and facilitating collaborative decision-making while maintaining user privacy. However, in Internet of Things (IoT) systems, the heterogeneity of devices and unstable network connections present significant challenges to the effective and efficient execution of FL tasks in real-world environments. To address these challenges, we propose an Adaptive Federated Meta Learning Framework with Multi-Objectives and Context-Awareness (AdaFML). This framework aims to achieve multiple objectives, including improving the performance of the FL global model, optimizing time efficiency, and enabling local model adaptation in dynamic and heterogeneous environments. Specifically, AdaFML extracts contextual information from each device, including its data distribution, computation, and communication conditions, to train a multimodal model that optimizes the FL task and time cost estimation, enhancing global model performance and time efficiency. Moreover, AdaFML fine-tunes two critical meta-learning parameters: the mixture ratio between local and global models and the selection weights for model aggregation. This enables adaptive local model updates across different devices while improving global model performance. Experimental results demonstrate that AdaFML boosts the effectiveness, efficiency, and adaptability of FL task execution in dynamic and heterogeneous environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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