Learning Client Selection Strategy for Federated Learning across Heterogeneous Mobile Devices
Bibliographic record
Abstract
The rapid growth of Internet of Things have yielded a remarkable increase in the volume of the data generated on client devices. This technological trend coincides with the rise of machine learning applications, which leverage user-generated data for large scale model training. In this context, Federated Learning (FL) has become a popular model for facilitating model training across edge devices in a decentralized fashion. However, the statistical diversity presented in the client data and performance heterogeneity existed among the user mobile device can seriously impact the accuracy of the result model and system performance of FL. This article first illustrates the state-of-the-art FL algorithms and investigates the major issues presented in the FL implementation, and then presents a novel FL algorithm that jointly optimizes both the model performance and implementation efficiency for the FL systems. Specifically, we propose an intelligent FL client selection scheme by leveraging the recent advance of Reinforcement Learning (RL) in solving complex control problems. The proposed solution, termed IntelliFL, can greatly improve both the accuracy performance and system performance of FL under the training environment with heterogeneous client devices.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".