Towards Sustainable IoT: A Digital Signature‐Enhanced Federated Learning Approach
Bibliographic record
Abstract
ABSTRACT Federated Learning (FL) is emerging as a premier paradigm for privacy‐preserved Machine Learning (ML), enabling devices to train models without central data pooling collaboratively. In the contemporary Internet of Things (IoT) landscape, characterized by escalating energy consumption and associated carbon footprint, FL is recognized not merely for its privacy features. Intrinsic to decentralized architectures such as FL, secure communication is based on digital signatures to guarantee integrity. This is particularly evident in sensitive sectors such as the Internet of Vehicles (IoV), banking, and healthcare. Integrating FL becomes imperative and intricate as these sectors are intertwined with the IoT fabric. Our study unveils “Secure Federated Learning Framework (SecFL),” a pioneering decentralized framework combining FL and sustainable computing. SecFL offers defences against adversarial attacks such as data poisoning and label flipping. Utilizing the Rivest‐Shamir‐Adleman (RSA) asymmetric encryption algorithm for securing digital communications and transactions, combined with ElGamal encryption and a private Ethereum blockchain, ensures enhanced client‐specific security. Our research emphasizes the formal modeling of adversarial dynamics using High‐Level Petri nets (HLPN) within the FL‐IoT ecosystem, balancing system dynamics and energy conservation. Our model consistently outperforms contemporary solutions in accuracy and time efficiency after validation. As IoT burgeons into domains like environmental monitoring, smart cities, and energy grids, the SecFL framework, fostering FL, optimizes energy utilization and bolsters resource efficiency. In our comparative analysis, the Elliptic Curve Digital Signature Algorithm (ECDSA) algorithm demonstrates superior transaction latency and verification time compared to RSA and Elliptic Curve Cryptography (ECC).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".