Overview
| Date | December 3, 2022 |
| Location | Room 298 - 299 |
| Workshop | https://nips.cc/virtual/2022/workshop/50001 |
Recent rapid development of machine learning has largely benefited from algorithmic advances, collection of large-scale datasets, and availability of high-performance computation resources, among others. However, the large volume of collected data and massive information may also bring serious security, privacy, services provisioning, and network management challenges. In order to achieve decentralized, secure, private, and trustworthy machine learning operation and data management in this “data-centric AI” era, the joint consideration of blockchain techniques and machine learning may bring significant benefits and have attracted great interest from both academia and industry. On the one hand, decentralization and blockchain techniques can significantly facilitate training data and machine learning model sharing, decentralized intelligence, security, privacy, and trusted decision-making. On the other hand, Web3 platforms and applications, which are built on blockchain technologies and token-based economics, will greatly benefit from machine learning techniques in resource efficiency, scalability, trustworthy machine learning, and other ML-augmented tools for creators and participants in the end-to-end ecosystems.
This workshop focuses on how future researchers and practitioners should prepare themselves to achieve different trustworthiness requirements, such as security and privacy in machine learning through decentralization and blockchain techniques, as well as how to leverage machine learning techniques to automate some processes in current decentralized systems and ownership economies in Web3. We attempt to share recent related work from different communities, discuss the foundations of trustworthiness problems in machine learning and potential solutions, tools, and platforms via decentralization, blockchain and Web3, and chart out important directions for future work and cross-community collaborations.
Topics include but are not limited to:
- Trustworthy ML systems via decentralization
- Privacy-preserving distributed ML systems
- Decentralized AI systems for real-world deployment
- Decentralized Data management in AI systems
- Distributed Consensus & Fault Tolerance Algorithms
- Resilient Federated Learning Systems
- Decentralized Finance (DeFi) and Decentralized Autonomous Organization (DAO)
- Decentralized FL frameworks & benchmarks
- Decentralized Data Science
- Decentralized training and inference for foundation models
Organizers
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| Jian Lou Applied Scientist, Amazon |
Stephen (Zhiguang) Wang America CTO, Amber Group |
Bo Li Assistant Professor, University of Illinois at Urbana-Champaign, Amazon Scholar |
Dawn Song Professor, UC Berkeley |