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AI-DCS 2024 : The 1st IEEE International Workshop on Generative, Incremental, Adversarial, Explainable AI/ML in Distributed Computing Systems


When Jul 16, 2024 - Jul 19, 2024
Where Jersey City, New Jersey (USA)
Submission Deadline Apr 14, 2024
Notification Due Apr 30, 2024
Final Version Due May 10, 2024
Categories    incremental learning   adversarial learning   explainable ai   generative ai

Call For Papers


AI-DCS 2024

The 1st IEEE International Workshop on Generative, Incremental, Adversarial, Explainable AI/ML in Distributed Computing Systems July 16-19, 2024 in Jersey City, New Jersey (USA)


The 1st IEEE International Workshop on Generative, Incremental, Adversarial, Explainable AI/ML in Distributed Computing Systems (AI-DCS 2024) will be held in conjunction with the 44th IEEE International Conference on Distributed Computing Systems (ICDCS) in Jersey City, New Jersey (USA) on July 16-19, 2024.

AI-DCS aims at the investigation of research results and at the systematic discussion of challenges at the intersection of Artificial Intelligence and Machine Learning (AI/ML) with Distributed Computing Systems.

AI-DCS 2024 will include original full-paper presentations and a keynote. The workshop attendees will be stimulated to participate in interesting discussions.


Submission site:

Paper Submission: April 14th, 2024

Acceptance Notification: April 30th, 2024

Camera-ready Papers: May 10th, 2024


Authors are invited to submit papers that fall into or are related to one or multiple topic areas listed below:

Generative AI/ML Models in Distributed Systems

- Generative AI for efficient management and monitoring of network resources
- Automatic network configuration with Generative AI
- Generative AI for Traffic Engineering
- Generative AI for improving network security
- Prompt Engineering for using Large Language Models (LLMs) in distributed systems
- Strategies for training generative models across distributed nodes
- Efficient deployment of generative models in distributed environments
- Load balancing for generative model inference
- Automatic generation of diverse datasets in distributed environments (e.g., industrial IoT, mobile, vehicular, cloud computing, and edge computing)

Incremental Learning in Distributed Systems

- AI/ML for handling dynamic data sources and network conditions
- Federated transfer-learning
- Adapting pre-trained models to distributed environments via transfer-learning
- Knowledge transfer between IoT devices
- Training meta-learning models in distributed environments
- Implementation of continuous learning algorithms in a decentralized fashion
- Edge-to-cloud communication for model updates
- Resource-efficient continual learning in IoT and edge devices

Adversarial Learning in Distributed Systems

- Adversarial threats in federated learning setups
- Privacy-preserving training strategies in distributed adversarial environments
- Secure AI/ML model deployment in distributed systems
- Adversarial defense mechanisms in distributed environments
- Trade-offs between security and model performance in decentralized systems
- Threat models for distributed applications based on AI/ML

Explainable AI in Distributed Systems

- Fairness, accountability, and transparency in AI/ML for networking
- Explainable AI techniques for distributed models
- Reliability of AI/ML methods in critical distributed applications
- Explainable machine learning models for network performance optimization
- Interpretability in AI/ML-based network traffic analysis and management tools
- Evaluation methods for explainable AI/ML in distributed systems
- Human-in-the-loop distributed systems


- AI/ML and its applications in distributed systems
- AI/ML and its applications to industrial IoT systems
- AI/ML and its applications to cloud and edge computing
- AI/ML and its applications to blockchain
- AI/ML and its applications for securing Distributed Computing Systems
- AI/ML for network anomaly and misuse detection
- ML and DL approaches for network traffic analysis and management


Authors are required to submit fully formatted, original papers (in PDF format). All workshop papers are limited to no more than 6 pages, including references, in the IEEE format aligned with the IEEE ICDCS 2024 main conference guidelines ( Each submission must be written in English, accompanied by a 75 to 200 words abstract that clearly outlines the scope and contributions of the paper.

The submission site is:

Accepted and presented papers will be published in the ICDCS Workshops proceedings and submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases. IEEE reserves the right to exclude a paper from distribution after the conference, including IEEE Xplore® Digital Library if the paper is not presented by the author at the conference.


- Yuval Shavitt, School of Electrical Engineering Tel-Aviv University, Israel (

- Antonio Pescapè, University of Napoli Federico II, Italy (

- Tal Shapira, The Hebrew University of Jerusalem, Israel (

- Antonio Montieri, University of Napoli Federico II, Italy (


- Elizabeth Liri, Saint Louis University, USA (


- Giampaolo Bovenzi, University of Napoli Federico II, Italy (


- Anat Bremler-Barr, Tel-Aviv University, Israel
- Walter Cerroni, Università di Bologna, Italy
- Haiming Chen, Chinese Academy of Sciences, China
- Tomáš Čejka, Faculty of Information Technology CTU in Prague, Czech Republic
- Domenico Ciuonzo, Università di Napoli Federico II, Italy
- Claudio Fiandrino, IMDEA Networks Institute, Spain
- Danilo Giordano, Politecnico di Torino, Italy
- Catalin Meirosu, Ericsson, Sweden
- Pham Tran Anh Quang, Huawei Technologies, France
- Solange Rito Lima, University of Minho, Portugal
- Kamal Singh, Telecom Saint Etienne, France
- Giancarlo Sperlì, Università di Napoli Federico II, Italy
- José Suárez-Varela, Telefonica Research, Spain
- Jonatan Krolikowski, Huawei Technologies, France

Note: if interested in being part of the TPC, please contact workshop organizers.

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