Topics for Interest (included, but not limited to)

• Web3 + AI, in particular, understanding the foundations of decentralized machine learning over decentralized data in web3

• Human-in-the-loop learning (i.e., reinforcement learning with human feedback, etc.).

• Adding generative AI and external knowledge integration.

• Large generative language models, in particular, multi-task, multimodal, multilingual models that can be augmented with external knowledge.

• Federated Learning, in particular the challenges security, privacy, robustness, heterogeneous datasets, personalization, fairness, lack of labels (unsupervised learning), resource constraints, aggregation/bandwidth bottleneck, adversarial attacks, scalability, and continual/incremental learning.

• Application domains and benchmarks for trustworthy machine learning, in particular applications in image processing, natural language processing, computer vision, healthcare, IoT, and data sciences.

• New information-theoretic and cryptographic approaches for secure and privacy-preserving learning and inference.

• Fairness, Bias, and Trust in decentralized and privacy-preserving machine learning, in particular, the challenges of establishing trust in federated learning domains, in which the users and the servers can behave autonomously and can be malicious.

• Hardware security and its role in trustworthy AI

Timeline

Release of CFP: 19 January 2023

Full Proposals Due: 27 April 2023 at 5pm PST

Announcements of Selected Projects: 27 June 2023

New Projects Start Date: 1 July 2023

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