1st Symposium on Networking and Artificial Intelligence (SNAI 2024)
Oct 18 - Oct 20, 2024
Zhejiang University, Hangzhou, China
About the Conference
The 1st Symposium on Networking and Artificial Intelligence (SNAI 2024) was the inaugural edition of the SNAI series, held in Hangzhou, China. It was founded to give a dedicated home to research at the two-way intersection of computer networking and artificial intelligence: AI applied to networks (learned congestion control, traffic engineering, anomaly detection) and networks built for AI (collective communication, distributed training, edge inference).
The inaugural edition received 87 submissions and accepted 24 papers, an acceptance rate of 27.6%, with authors from 21 countries and roughly 180 attendees.
This edition is closed and archived. Please see SNAI 2026 for the current call for papers.
Submission Guidelines
Papers must be submitted as PDF in the two-column IEEE conference format. Full papers are limited to 8 pages of technical content plus unlimited pages of references; short papers are limited to 4 pages plus references.
SNAI uses double-blind review. Submissions must not reveal author names or affiliations, and self-citations must be phrased in the third person. Papers that are not properly anonymized may be desk-rejected.
Submitted work must be original and must not be under review at another venue at the time of submission. Authors are encouraged to submit artifacts (code, datasets, measurement traces) to support reproducibility.
Review Guidelines
Evaluate each submission on originality, technical soundness, experimental rigour, clarity of presentation, and relevance to the networking/AI intersection that defines SNAI.
Every paper receives at least three independent reviews. Please justify your score with specific, actionable comments the authors can respond to, and flag any reproducibility or ethical concerns in the confidential comments to the chairs.
Do not attempt to deanonymize authors, and declare any conflict of interest to the program chairs immediately.
Tracks
AI for Network Management and Optimization
Learned congestion control, traffic engineering, routing, resource allocation, and closed-loop network automation.
Edge and In-Network Inference
Model placement, partitioning, and serving at the network edge and inside the data plane.
Distributed and Federated Learning over Networks
Communication-efficient training, gradient compression, and federated learning under realistic network constraints.
Machine Learning for Network Security
Intrusion and anomaly detection, encrypted traffic analysis, and adversarial robustness of network classifiers.
Important Dates
Past Edition
This edition has concluded and is kept here as a permanent record. Its program, proceedings and committees remain available on the conference website.
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