As artificial intelligence (AI) research advances, the key obstacle to widespread AI adoption has shifted from technical challenges to gaining stakeholders' trust. Building AI techniques that are fair, transparent, and robust has been recognized as a viable means of enhancing confidence in AI. However, addressing data privacy and user confidentiality concerns presents an additional layer of complexity. Prominent conferences like ICME have acknowledged the necessity of developing methods that accommodate data privacy protection goals. Stricter regulations such as the GDPR require revising the existing centralized AI training paradigm to ensure regulatory compliance.

Federated Learning (FL) offers a learning paradigm that facilitates collaborative training of machine learning models without sharing data from individual data silos. This approach enables AI to thrive in privacy-focused regulatory environments. FL empowers self-interested data owners to collaboratively train models, making end-users active contributors to AI solutions. Currently, FL relies on a central trusted entity to coordinate co-creators, which can become a single point of failure. The assumption that all co-creators receive the same final FL model regardless of their contributions introduces unfairness and hampers FL adoption. 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.

This special session aims to provide a timely collection of research updates to benefit researchers and practitioners working in trustworthy federated learning systems for multimedia. Topics of interest include but are not limited to:

Applications of Federated Learning in Multimedia
Auction-based Federated Learning
Auditable Federated Learning
Client Selection in Federated Learning
Data Selection in Federated Learning
Decentralized Federated Learning
Fairness-Aware Federated Learning
Feature Selection in Federated Learning
Federated Graph Neural Networks
Federated Learning and Foundation Models
Federated Learning for Non-IID Data
Incentive Mechanisms in Federated Learning Systems
Interpretability in Federated Learning
Large-Scale Federated Learning
Quantum Federated Learning
Reputation-aware Federated Learning
Robustness for Federated Learning
Social Responsibility in Federated Learning Systems
Transferable Federated Learning
Trustable Federated Learning
Verifiable Federated Learning


Submission Instructions:
Information on paper submission can be found here: https://2024.ieeeicme.org/author-information-and-submission-instructions/

All accepted papers will be included in the ICME 2024 proceedings, published on the IEEE Xplore Digital Library.


Organizers:
Guodong Long (University of Technology Sydney, Australia)
Han Yu (Nanyang Technological University, Singapore)