The First International Workshop on
Federated Knowledge Discovery and Data Mining (FedKDD 2024)
in conjunction with the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), Barcelona, Spain, August 25-29, 2024.
Workshop Program
The workshop will be held on August 26, 2024. All times are in Barcelona time (CEST). For details, please refer to the program page.
Updates
- (TBD) Workshop proceedings will be published in ACM Digital Library.
- (TBD) Best paper awards will be announced.
- (July 15, 2024) Paper notification is out.
- (June 10, 2024) Submission deadline is extended to June 20, 2024.
- (May 20, 2024) Our workshop website is online.
Overview
Recent years have witnessed a paradigm shift in data collaboration, moving from centralized data lakes to decentralized ecosystems. This shift is driven by the increasing need for privacy-preserving data analytics and the proliferation of data across various organizations and devices. Federated learning (FL), a decentralized machine learning approach, has emerged as a key technology in this context. While FL has shown great promise in various applications, its full potential in complex data mining and knowledge discovery tasks remains largely untapped. This workshop aims to bridge this gap by bringing together researchers and practitioners from academia and industry to explore the latest advancements and challenges in federated knowledge discovery and data mining (FedKDD).
The FedKDD workshop will focus on the intersection of federated learning, data mining, and knowledge discovery. We solicit original research papers on a wide range of topics, including but not limited to:
- Federated algorithms for data mining tasks, such as clustering, classification, regression, association rule mining, and anomaly detection.
- Federated knowledge discovery, including federated knowledge graph construction, federated knowledge representation, and federated reasoning.
- Privacy-preserving techniques for FedKDD, such as differential privacy, secure multi-party computation, and homomorphic encryption.
- Theoretical foundations of FedKDD, including convergence analysis, communication efficiency, and incentive mechanism design.
- System design and implementation of FedKDD platforms.
- Applications of FedKDD in various domains, such as healthcare, finance, transportation, and social media.
The workshop will feature a mix of invited talks, regular paper presentations, and a panel discussion. The invited talks will be given by leading experts in the field of federated learning and data mining. The regular paper presentations will provide an opportunity for researchers to share their latest work and receive feedback from the community. The panel discussion will focus on the future of FedKDD and its potential impact on society.
Submission
We invite submissions of original research papers, up to 9 pages in length (including references), in the standard ACM format. The submission website is https://cmt3.research.microsoft.com/FedKDD2024.
All submitted papers will be peer-reviewed by at least three program committee members. The accepted papers will be published in the workshop proceedings and will be available in the ACM Digital Library.
Important Dates
- Submission Deadline: June 20, 2024 (11:59 PM, AoE)
- Notification of Acceptance: July 15, 2024
- Camera-Ready Version Due: August 1, 2024
- Workshop Date: August 26, 2024
Organizers
- Zheng Chai, Carnegie Mellon University
- Hanlin Lu, Carnegie Mellon University
- Yifei Wang, Carnegie Mellon University
- Jun-Yuan Hsieh, SONY AI
- Carl Yang, Emory University
- Lianli Gao, Zhejiang University
Contact
For any questions, please contact the organizers at fedkdd-organizers@googlegroups.com.