FL-AAAI-22: International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022 (FL-AAAI-22) TBD Vancouver, Canada, February 22-March 1, 2022 |
Conference website | http://federated-learning.org/fl-aaai-2022/ |
Submission link | https://easychair.org/conferences/?conf=fl-aaai-22 |
Submission deadline | November 30, 2021 |
Federated learning (FL) is one promising machine learning approach that trains a collective machine learning model using sharing data owned by various parties. It leverages many emerging privacy-preserving technologies (SMC, Homomorphic Encryption, differential privacy, etc.) to protect data owner privacy in FL. It has been gained popularity in some domains such as image classification, speech recognition, smart city, and healthcare. However, FL also faces multiple challenges that may potentially limit its applications in real-world use scenarios. For example, FL is still at the risk of various kinds of attacks that may result in leakage of individual data source privacy or degraded joint model accuracy. In other words, many existing FL solutions are still exposed to various security and privacy threats. This workshop aims to bring together FLresearchers and practitioners to address the additional security and privacy threats and challenges in FL To make its mass adoption and widespread acceptance in the community. For example, privacy-specific threats in FL, training/inference phase attacks; data poisoning, model poisoning, how to handle Non-IID data without affecting the model performance, lacking trust from the FL participant, how to gain confidence by interpreting FL model, scheme of contributions and rewards to FL participants for improving anFL model, social and corporate responsibility towards the adoption of FL, imbalance data among FL participants, methods to verify and proof the correctness of FL computation, etc. The discussion in the workshop can lead to implementing FL solutions that are more accurate, robust, and interpretable, gain the trust of the FL participants.
Submission Guidelines
Each submission can be up to 9 pages including references. The submitted papers must be written in English and in PDF format according to the AAAI-22 template. All submitted papers will be under a single-blinded peer review for their novelty, technical quality, impact, reproducibility, and so on. Submission will be accepted via the Easychair submission here.
List of Topics
- Interpretable Federated Learning
- The trade-Off between Privacy-Preserving and Explainable Federated Learning
- Federated Learning Multi-Party Computation
- Federated Learning Homomorphic Encryption
- Federated Learning Differential Privacy
- Federated Transfer Learning
- Federated Learning Personalization Techniques
- Federated Learning Attacks and Defenses
- Federated Learning Blockchain Network
- Federated Learning Secure Aggregation
- Federated Learning Fairness and Accuracy
- Federated Learning with Non-IID Data
- Federated Learning Incentive Mechanism
- Federated Learning Meets Mean-Field Game Theory
- Federated Learning-based Corporate Social Responsibility
- Social Responsible Federated Learning
- Decentralized Federated Learning
- Vertical Federated Learning
Committees
Organizing committee
- Qiang Yang (Hong Kong University of Science and Technology)
- Sin G. Teo (Insitute for Infocomm Research, Singapore)
- Han Yu (Nanyang Technological University, Singapore)
- Lixin Fan (WeBank, China)
- Chao Jin (Insitute for Infocomm Research, Singapore)
- Le Zhang (University of Electronic Science and Technology of China)
- Yang Liu (Tsinghua Unversity, China)
- Zengxiang Li (Digital Research Institute, ENN Group, China)
- Xiuyi Fan (Nanyang Technological University, Singapore)
Invited Speakers
- TBA
Publication
Accepted papers will also be invited to submit to a special issue of IEEE Transactions on Big Data.
Venue
TBD
Contact
For enquiries, please email to: fl-aaai-22(at)easychair(dot)org
Sponsors
TBA