中国计算机学会（CCF）推荐学术会议-B（计算机体系结构/并行与分布计算/存储系统）：ICDCS 2026

原创于 2025-11-03 14:27:36 发布 · 472 阅读

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IEEE ICDCS 2026

As one of the IEEE Computer Society’s flagship conferences, the annual IEEE International Conference on Distributed Computing Systems (ICDCS) is a premier international forum for researchers, developers, and users to present, discuss, and exchange the cutting edge ideas on topics related to any aspects of Distributed Computing Systems.

重要信息

CCF推荐：B（计算机体系结构/并行与分布计算/存储系统）

录用率：19.6%（104/529，2025年）

时间地点：2026年7月20日-首尔·韩国

截稿时间：2025年12月5日

Call for Papers

Edge Computing, Mobile Computing, Distributed Computing.

Integrating Distributed Systems with Cloud Computing, Fog Computing, Multi-Tier Computing, and Over-the-Air Computation.

Distributed Algorithms.

Big Data, Models & Systems.

Distributed Fault Tolerance.

Distributed OS and Middleware.

Internet of Things (IoT), Cyber-Physical Systems (CPS).

Security, Privacy and Trust.

Blockchains/Databases.

Distributed Systems for AI/ML.

Deployed/Emergent Applications & Infrastructures, Digital Twins Computing Systems.

Real-Time Computing Distributed Systems.

double-blind

The paper review process is double-blind. Authors are required to take all reasonable steps to preserve the anonymity of their submission. The submission must not include author information and must not include citations or discussion of related work that would make the authorship apparent.

While authors can upload their paper to institutional or other preprint repositories such as arXiv.org before reviewing is complete, we generally discourage this since it places anonymity at risk. If authors decide to upload their paper to a preprint site, they must make sure that the title and abstract of their submission to ICDCS are different from the title and abstract of the preprint version, so that it is not immediately obvious that the two versions are by the same authors and with the same content.

To encourage reproducibility, we encourage the authors, whenever it is possible, to include in their paper a link to an anonymised GitHub repository with all source code, scripts and data needed for the reproduction of their results.