Federated Learning With Selective Knowledge Distillation Over Bandwidth-constrained Wireless Networks
Bibliographic record
Abstract
Artificial Intelligence (AI) applications on Internet of Things (IoT) networks often involve relaying generated data to a server for deep learning training, which poses security risks to users' data. Federated Learning (FL) offers a distributed model training paradigm in which local data are kept at the edge and locally trained models are exchanged and aggregated by a server over several rounds to produce a global model. While successful, standard FL algorithms do not support heterogeneous local model design, an essential requirement, especially for resource-limited edge devices. Recently, Knowledge Distillation-based FL algorithms have provided model-agnostic FL to enable clients to independently design their local model and share soft labels instead of model parameters. KD-based FL algorithms are computationally expensive due to additional distillation training. We propose Federated Learning with Selective Knowledge Distillation (FedSKD) to address the limitations of system heterogeneity; and computation and communication demands. We evaluate different aspects of the proposed algorithm relative to baseline FL algorithms. Results show that FedSKD incurs significantly less per-round computation time and communication overhead relative to the considered model-based and KD-based FL algorithms.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.010 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".