DistLLM' 26

The 1st Workshop on Reimagining Distributed Computing for LLMs (DistLLM) aims to tackle the pressing challenges in the scalable, efficient, and reliable distributed training, fine-tuning, and inference of large language models (LLMs) and deep neural networks (DNNs). This workshop brings together leading experts in distributed systems, networking, and AI to collaboratively explore innovative solutions across multiple critical domains. These include advanced multi-dimensional parallelism techniques encompassing data, tensor, pipeline, and expert parallelism; communication and synchronization protocols optimized for efficient collective communication and congestion control; and intelligent distributed scheduling strategies that address multi-tenant resource allocation and adaptive scheduling under communication bottlenecks.

Further, the workshop emphasizes network-aware system design to ensure load-balanced traffic and topology-aware flow control, alongside developing resilience and fault tolerance methods such as checkpointing and anomaly detection for long-running training jobs. Recognizing the growing complexity of LLM training infrastructure, the event also addresses emerging challenges of multi-cluster training spanning data centers and edge clusters, including inter-domain and wide area network scheduling. Additionally, specialized topics on LLM deployment tailored for edge computing environments—such as fine-tuning tiny LLMs and optimizing for heterogeneous compute devices—are featured.

Through a comprehensive program of refereed papers, keynote talks, expert panels, lightning talks, and demonstrations, the DistLLM workshop seeks to advance the state-of-the-art in distributed systems infrastructure for AI while leveraging LLM capabilities to innovate distributed computing itself. By fostering interdisciplinary collaboration, this workshop intends to drive transformative research that meets the extraordinary demands of modern AI workloads and bridges key gaps in scalable, efficient, and resilient distributed model training and deployment.

Paper Submissions: 

All papers must be original and not simultaneously submitted to another journal or conference. All papers will be peer-reviewed using a double-blind peer-review process by at least three members of the program committee. Submissions should be a complete manuscript. DistLLM accepts Full papers: 6 pages in ACM Conference format (including title, abstract, figures, and references).

Workshop Paper Format Guidelines (ACM Style Adaptation)

• Papers should be formatted in double-column, single-spaced layout using a 10-point font size on standard 8.5 x 11-inch (US letter) pages.

• Submissions are anonymous. The conference will employ a lightweight double-blind reviewing process. Manuscripts should not include author names and affiliations. 

• Submissions should not reveal the identity of the authors in any way. Authors should ensure that any references to their own related work are in the third person (e.g., not “We build on our previous work …” but rather “We build on the work of …”).

• Authors are required to use the official ACM conference templates for manuscript preparation, available in both MS Word and LaTeX formats.

• ACM templates ensure compliance with ACM’s publication standards and can be found here:
  https://www.acm.org/publications/proceedings-template

Papers are to be submitted electronically in PDF format. Submitted papers should not have appeared in or be under consideration for a different workshop, conference or journal. All accepted papers need to be presented at the workshop by one of the authors.

All accepted papers (subject to post-review revisions) will be published in the ICDCN 2026 companion proceedings.

Submission Link: https://easychair.org/conferences?conf=icdcn2026
Select WS2: 1st Workshop on Reimagining Distributed Computing for LLMs for submissions.

Here are the specific ACM links related to the policies and guidelines mentioned for the workshop papers:

1. ACM Policy on Authorship:
   https://www.acm.org/publications/policies/new-acm-policy-on-authorship

2. ACM Policy on the use of Generative AI in papers (FAQ):
   https://www.acm.org/publications/policies/frequently-asked-questions

3. ACM Open Publication Model and FAQs for International Conference Proceedings Series (ICPS):
   https://www.acm.org/publications/icps/faq

4. List of Institutions Participating in the ACM Open Program:
   https://libraries.acm.org/acmopen/open-participants

5. Guidance on Article Processing Charges (APC) and publishing in the ACM Digital Library:
   https://www.acm.org/publications/icps/author-guidance

6. ICDCN 2026 Main Conference Paper Submission Guidelines (including double-blind review process):
   https://sites.google.com/view/icdcn2026/submissions/call-for-paper?authuser=0

These links will help authors and organizers ensure compliance with ACM's policies on authorship, generative AI usage, open access publishing, and paper submission guidelines.