Distributed Learning and Inference Systems: A Networking Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it