Call for Papers

We invite submissions for our upcoming Special Session: Federated Learning for Data Mining and Data Management at The 20th International Conference Advanced Data Mining and Applications 2024 (ADMA 2024).

Federated Learning for Data Mining and Data Management

In the digital era, data has become a crucial force driving technological progress. However, with the explosive growth of data volumes and the increasing demand for privacy protection, achieving efficient data mining and data management without compromising the privacy of raw data has become a challenge. Federated learning provides a novel approach to address this issue.

Federated learning enables multiple participants to train models directly on edge devices without sharing the original data. This approach not only safeguards data privacy and security but also allows for faster extraction of valuable information from distributed edge devices. Additionally, shifting computational tasks from central servers to edge devices reduces the burden on central servers, enhancing the stability and reliability of the entire system.

Furthermore, federated learning offers more flexible data management options. Participants can train models locally and choose to store data either locally or on central servers based on specific requirements, thereby meeting diverse data management and application needs.

Scope

This special track aims to bring together researchers, practitioners, and experts to explore the latest advancements, challenges, and opportunities in this domain by promoting cutting-edge research and innovation in federated learning for data mining and data management, facilitating knowledge sharing and cross-disciplinary collaborations, discussing real-world applications and case studies, and identifying emerging trends and future directions. We welcome contributions from a wide range of topics, including but not limited to:

· Federated Learning Algorithms and Theory: Including the design, optimization, and convergence analysis of federated learning algorithms at the theoretical level.

· Applications of Federated Learning in Data Mining: Exploring the application of federated learning in data mining tasks such as image processing, natural language processing, recommendation systems, and anomaly detection.

· Applications of Federated Learning in Data Management: Investigating the use of federated learning techniques to achieve data security, privacy protection, efficient storage, and querying in distributed databases, cloud computing, edge computing, and other environments.

· Federated Learning Systems and Practices: Sharing practical experiences in the implementation, deployment, and performance optimization of federated learning systems, as well as challenges and solutions encountered in practical applications.

· Security and Privacy Protection: Studying how to protect the security and privacy of user data during the federated learning process, including differential privacy, encryption techniques, and more.

· Cross-domain and Interdisciplinary Research: Encouraging cross-domain and interdisciplinary research, such as the intersection of federated learning with artificial intelligence, blockchain, the Internet of Things, and other fields.

Formatting Guidelines

We welcome English-language papers containing original and unpublished contributions to the fields of data mining and related areas. Manuscripts should adhere to the LNAI (Lecture Notes in Artificial Intelligence) format. For the template and detailed instructions on LNCS style, please refer to Springer's Author Instructions. Papers should adhere to the main conference guidelines, ensuring they do not exceed 15 pages in LNAI format. Submissions undergo a double-blind review process for ADMA2024. This means:

· Author identities and affiliations remain undisclosed to reviewers throughout the review process.

· Authors must prepare and submit blinded manuscripts that conceal author and affiliation information. Specific guidelines are outlined below.

· Both authors and reviewers are expected to make sincere efforts to prevent accidental de-blinding of any submission.

· All submitted papers must adhere to the following rules. Non-compliance may result in automatic rejection.

Submission Guidelines

Authors are invited to submit original research papers, case studies, and technical reports aligned with the theme of Data Mining, Data Management and Federated Learning. Submissions should adhere to the conference's formatting guidelines and be submitted through the CMT online submission system. All submissions will undergo a rigorous peer-review process to ensure quality and relevance. When submitting your manuscript, please choose the "Special Session Track" option and select the area of "Special Session: Federated Learning for Data Mining and Data Management."

Important Dates

· Paper Submission Deadline: 15^th June, 2024 (AoE)  20^th July, 2024(AoE)

· Notification of Acceptance: 1^st August, 2024 (AoE)

· Camera-Ready Paper Due: 15^th August, 2024 (AoE)

· Conference Dates: 3^rd~5^th December, 2024 (AEST)

Track Chairs

· Yipeng Zhou, Macquarie University, Australia

· Zhiwang Zhang, NingboTech University, China

· Shui Yu, University of Technology Sydney, Australia

Program Committee

· Rui Su, Shanghai AI lab, China

· Zhenghao Chen, The University of Sydney, Australia

· Long Chen, Guangdong University, China

· Miao Hu, Sun Yat-sen University, China

· Weifeng Sun, Dalian University of Technology

· Jinyang Guo, Beihang University