Federated Learning and Proactive Computation Reuse at the Edge of Smart Homes
Bibliographic record
Abstract
Edge-based technologies have emerged as a key enabler to empower low-latency services and incorporate machine learning techniques for learning/inference. However, transferring user data to the edge server to conduct learning could violate data privacy and overburden the network. In addition, the server could receive multiple redundant tasks for inference which leads to redundant computations. In this article, we study both communication and computation issues in edge networks by emphasizing data privacy in a smart home scenario. We design an architecture that incorporates federated edge learning to promote data privacy and a node weighting and dropping scheme to select the appropriate participating devices with quality data and therefore improve the training and reduce communication cost. We further apply Long Short-Term Memory to predict future tasks and proactively store them locally at the edge device. We adopt the computation reuse concept to satisfy incoming tasks with less-to-no computation and thus eliminating redundant computation and further decreasing the computation cost. Simulation results based on real-world dataset show the effectiveness and efficiency of the proposed architecture. The training phase is reached with few iterations, while computation and communication are reduced by up to 80% and 70%, respectively, compared with existing schemes while data privacy is promoted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".