Enhancing cooperative multi-agent reinforcement learning through the integration of R-STDP and federated learning
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a novel approach to enhance the stability and efficiency of R-STDP in the context of federated learning. The primary objective is to stabilize the unbounded growth of R-STDP and make it more responsive to real-time changes. The methodology involves integrating R-STDP with Spiking Neural Networks and employing the norm of the neural network model for adjusting weighted aggregation in federated learning systems. The proposed method incorporates a mechanism where weights decay over time, depending on the duration since the agent last published its model. Additionally, the sampling time is dynamically adjusted based on the Euclidean norm, which measures the distance between the weight matrices of the agents and the server. The results demonstrate that the proposed event-triggered federated learning method significantly enhances learning speed and performance. At the same time, the dynamic aggregation interval efficiently reduces communication between the agents and the central server, especially after model convergence. This research presents a significant advancement in federated learning and offers a more stable, responsive, and efficient learning process.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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