Distributed Learning and Inference Systems: A Networking Perspective
Bibliographic record
Abstract
Artificial intelligence (AI) has made significant strides, achieving and in some cases surpassing human-level performance. This has primarily been accomplished through the centralized training of static models that are then stored in centralized clouds for inference. Centralized approaches present several challenges, including privacy concerns, high storage demands, vulnerability to single points of failure, and substantial resource requirements. These limitations sparked interest in developing decentralized approaches to alleviate some of these shortcomings. Yet, decentralization introduces additional complexities, particularly in managing multiple dynamic components. Regardless of whether AI systems are centralized or decentralized, it is clear that a robust enabling infrastructure is essential for reliable and scalable operation. While simpler infrastructures may suffice for centralized approaches, distributed learning and inference require more sophisticated architectural designs. To address this gap, this paper proposes a network-inspired distributed AI service architecture, termed as Data and Dynamics-Aware Inference and Training Network (DA-ITN), designed to support mobility and decision-making across diverse AI scenarios. The components and functions of DA-ITN are explored, its potential role in the future of AI is discussed, and the various challenges and research opportunities required to realize such an architecture are identified.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".