Tensor-Enabled Communication-Efficient and Trustworthy Federated Learning for Heterogeneous Intelligent Space–Air–Ground-Integrated IoT
Bibliographic record
Abstract
Federated learning (FL) could provide a promising privacy-preserving intelligent learning paradigm for space–air–ground-integrated Internet of Things (SAGI-IoT) by breaking down data islands and solving the dilemma between data privacy and data sharing. Currently, adaptivity, communication efficiency and model security are the three main challenges faced by FL, and they are rarely considered by existing works simultaneously. Concretely, most existing FL works assume that local models share the same architecture with the global model, which is less adaptive and cannot meet the heterogeneous requirements of SAGI-IoT. Exchanging numerous model parameters not only generates massive communication overhead but also poses the risk of privacy leakage. The security of FL based on homomorphic encryption with a single private key is weak as well. Given this, this article proposes a tensor-empowered communication-efficient and trustworthy heterogeneous FL, where various participants could choose suitable heterogeneous local models according to their actual computing and communication environment, so that clients with different capabilities could do what they are good at. Additionally, tensor train decomposition is leveraged to reduce communication parameters while maintaining model performance. The storage requirements and communication overhead for heterogeneous clients are reduced further. Finally, the homomorphic encryption with double trapdoor property is utilized to provide a robust and trustworthy environment, which can defend against the inference attacks from malicious external attackers, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">honest-but-curious</i> server and internal participating clients. Extensive experimental results show that the proposed approach is more adaptive and can improve communication efficiency as well as protect model security compared with the state-of-the-art.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Open science | 0.010 | 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; both teacher heads agree on what is shown here.
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".