SS03: Trustworthy Federated Learning for Multimedia
Motivation & significance
•Building AI techniques that are fair, transparent, and robust has been recognized as a viable means of enhancing confidence in AI.
•Trustworthy federated learning emerges as a promising direction, fostering open collaboration among FL co-creators while upholding transparency, fairness and robustness, without compromising sensitive local data.
Topics of interest
•Applications of Federated Learning in Multimedia
•Auction-based/Auditable Federated Learning
•Client Selection, Data Selection, Feature Selection in Federated Learning
•Decentralized, Fairness-Aware Federated Learning
•Federated Graph Neural Networks, Federated Learning and Foundation Models
•Federated Learning for Non-IID Data
•Incentive Mechanisms, Social Responsibility in Federated Learning Systems
•Interpretability in Federated Learning
•Large-Scale, Quantum, Reputation-aware Federated Learning
•Robustness for Federated Learning Transferable, Trustable, Verifiable Federated Learning
Organizers
Han Yu, Associate Professor, Nanyang Technological University, Singapore, email: han.yu@ntu.edu.sg
Guodong Long, Associate Professor, University of Sydney, Australia, email: guodong.long@uts.edu.au