Towards On-Demand Model and Client Deployment in Federated Learning with Reinforcement Learning
Bibliographic record
Abstract
In Federated Learning (FL) solutions, a significant challenge lies in the limited accessibility of data sourced from diverse locations and user types, primarily due to restricted user participation. Expanding client access and diversifying data enrich models by incorporating diverse perspectives and enhancing adaptability. Increasing the client pool through volunteer devices and diversifying data enables the applicability of FL in previously static or inaccessible areas. While past research has focused on improving client selection techniques, the dynamic nature of the environment may render certain devices inaccessible as FL clients, impacting data availability and the effectiveness of current client selection methods. To overcome this problem, we propose our On-Demand solution, prioritizing the on-the-fly deployment of new clients using Docker Containers. Frequent FL model updates present challenges in environment setup, particularly when FL applications and available volunteer nodes change. Thus, this paper introduces an On-Demand solution targeting client availability and selection in FL by introducing a Reinforcement Learning-based solution driven by the need for swift model updates and the complexities of container deployment. The RL strategy employs a Markov Decision Process (MDP) framework with a Master Learner and a Joiner Learner engaging in offline and online learning based on server log data reflecting client and application demands. Simulated experiments and simulations demonstrate the adaptability of our architecture to environmental changes and On-Demand requests, along with its potential to enhance client availability, capability, accuracy, and learning efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".