Reliable Federated Learning with Auction-Based Incentives at the Extreme Edge
Bibliographic record
Abstract
Extreme Edge Computing (XEC) is a serverless edge computing paradigm where computational tasks are offloaded to and from extreme edge devices (XEDs). XEDs, a subset of IoT devices that consists of consumer-owned devices capable of offering computational resources. Being data-rich, XEDs can facilitate the training of more accurate Machine Learning (ML) models. However, their unpredictable computational behavior, which follows the consumers’ usage, and transient availability pose challenges that traditional Federated Learning (FL) approaches may struggle to address. To this end, we propose a new framework for decentralized FL in XEC systems designed to address the computational reliability of XEDs and optimize the computational resource allocation. Moreover, to encourage XEDs’ participation in the FL training process, we introduce an Auction-based incentive mechanism. This mechanism models the interactions between XEDs, considering both the computational characteristics and the data quality of XEDs. Furthermore, we present two solution approaches: an optimization approach and a heuristic approach, each introducing a complexity-performance trade-off. Finally, we evaluate and demonstrate the effectiveness of our proposed framework in improving the performance and reliability of XEDs in decentralized learning environments.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".