FL-IJCAI'22: International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22) Virtual Vienna, Austria, July 23-29, 2022 |
Conference website | https://federated-learning.org/fl-ijcai-2022/ |
Submission link | https://easychair.org/conferences/?conf=fl-ijcai-22 |
[Call for Papers]
Federated Learning (FL), a learning paradigm that enables collaborative training of machine learning models in which data reside and remain in distributed data silos during the training process. FL is a necessary framework to ensure AI thrive in the privacy-focused regulatory environment. As FL allows self-interested data owners to collaboratively train machine learning models, end-users can become co-creators of AI solutions. To enable open collaboration among FL co-creators and enhance the adoption of the federated learning paradigm, we envision that communities of data owners must self-organize during FL model training based on diverse notions of trustworthy federated learning, which include, but not limited to, security and robustness, privacy-preservation, interpretability, fairness, verifiability, transparency, auditability, incremental aggregation of shared learned models, and creating healthy market mechanisms to enable open dynamic collaboration among data owners under the FL paradigm. This workshop aims to bring together academic researchers and industry practitioners to address open issues in this interdisciplinary research area. For industry participants, we intend to create a forum to communicate problems are practically relevant. For academic participants, we hope to make it easier to become productive in this area. The workshop will focus on the theme of building trustworthiness into federated learning to enable open dynamic collaboration among data owners under the FL paradigm, and make FL solutions readily applicable to solve real-world problems.
Topics of interest include, but are not limited to:
Techniques:- Adversarial learning, data poisoning, adversarial examples, adversarial robustness, black box attacks- Architecture and privacy-preserving learning protocols- Auctions in federated learning- Auditable federated learning- Automated federated learning- Explainable federated learning- Fairness-aware federated learning- Federated learning and distributed privacy-preserving algorithms- Federated transfer learning- Human-in-the-loop for privacy-aware machine learning- Incentive mechanism and game theory for federated learning- Interpretable federated learning- Model merging and sharing- Personalization in federated learning- Privacy-aware knowledge driven federated learning- Privacy-preserving techniques (secure multi-party computation, homomorphic encryption, secret sharing techniques, differential privacy) for machine learning- Robustness in federated learning- Security for privacy, privacy leakage verification and self-healing etc.- Trade-off between privacy, safety, effectiveness and efficiency- Transparent federated learning- Verifiable federated learning
Applications:- Algorithm auditability- Approaches to make GDPR-compliant AI- Data value and economics of data federation- Open-source frameworks for privacy-preserving distributed learning- Safety and security assessment of federated learning- Solutions to data security and small-data challenges in industries- Standards of data privacy and security
[Submission Instructions]
Each submission can be up to 6 pages of contents plus up to 2 additional pages of references and acknowledgements. The submitted papers must be written in English and in PDF format according to the IJCAI'22 template (https://www.ijcai.org/authors_kit). All submitted papers will be under a single-blinded peer review for their novelty, technical quality and impact. The submissions can contain author details. Submission will be accepted via the Easychair submission website.
Easychair submission site: https://easychair.org/conferences/?conf=fl-ijcai-22
[Publications]
For consideration of a post workshop LNAI publication, the organizing committee will invite a subset of accepted workshop papers to be extended and re-reviewed. More information regarding publications will be released at a later date.