Time	Title	Speakers
10:00-10:15am	Opening Remarks	Bijan Davari (IBM Fellow/Vice President of IBM Research)
10:15-10:45pm	Invited Talk 1: Federated Learning in Resource-Constrained Edge Computing Systems	Shiqiang Wang, IBM Research, USA
10:45-11:15pm	Invited Talk 2: Efficient and Accurate Approaches for Privacy-Preserving Federated Learning	Nathalie Baracaldo, IBM Research, USA
11:15-11:30pm	Coffee Break
11:30-12:00pm	Invited Talk 3: Analyzing Federated Learning Through An Adversarial Lens	Supriyo Chakraborty, IBM Research, USA
12:00-13:00pm	Lunch Break (Provided)
13:00-13:30pm	Invited Talk 4: Vertical(cross-silo) federated learning : some concepts and applications	Yang Liu (on behalf of Qiang Yang), Hong Kong University of Science and Technology and WeBank CAO, China
13:30-14:00pm	Invited Talk 5: Federated learning at Google: systems, algorithms, and applications	Keith Bonawitz, Google Research, USA
14:00-14:30pm	Invited Talk 6: Split Learning: A new resource efficient alternative for distributed machine learning	Praneeth Vepakomma (on behalf of Ramesh Raskar), Massachusetts Institute of Technology, USA
14:30-15:00pm	Invited Talk 7: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data	Mingyi Hong, University of Minnesota, USA
15:00-15:15pm	Coffee Break
15:15-15:45pm	Invited Talk 8: Incentives for Federated Learning	Boi Faltings, Swiss Federal Institute of Technology Lausanne, Switzerland
15:45-16:15pm	Invited Talk 9: Bayesian Nonparametric Fusion of heterogeneous models	Mikhail Yurochkin, IBM Research, USA
16:15-17:15pm	Panel: Data Privacy and Regulatory Issues in AI: Enterprise and Customer Perspectives	Shahrokh Daijavad, Qiang Yang, Keith Bonawitz, Ramesh Raskar, Shiqiang Wang

Privacy and security have become critical concerns in recent years, particularly as companies and organizations increasingly collect detailed information about their products and users. This information can enable machine learning methods that produce better products. However, it also has the potential to allow for misuse, especially when private data about individuals is involved. Recent research shows that privacy and utility do not necessarily need to be at odds, but can be addressed by careful design and analysis. The need for such research is reinforced by the recent introduction of new legal constraints, led by the European Union’s General Data Protection Regulation (GDPR), which is already inspiring novel legislative approaches around the world such as Cyber-security Law of the People’s Republic of China and The California Consumer Privacy Act of 2018.

An approach that has the potential to address a number of problems in this space is Federated Learning (FL). FL is an ML setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. Organizations and mobile devices have access to increasing amounts of sensitive data, with scrutiny of ML privacy and data handling practices increasing correspondingly. These trends have produced significant interest in FL, since it provides a viable path to state-of-the-art ML without the need for the centralized collection of training data – and the risks and responsibilities that come with such centralization. Nevertheless, significant challenges remain open in the FL setting, the solution of which will require novel techniques from multiple fields, as well as improved open-source tooling for both FL research and real-world deployment.

This one-day workshop aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to address above challenges. The workshop will consist of 12 invited talks on a wide variety of methods and applications. This workshop intends to share visions of investigating new approaches, methods, and systems at the intersection of Federated Learning and real-world applications.

Topics of interest (including but not limited to)

The invited talks will cover innovative research and applications around the topics below. These talks introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain.

• Architecture and privacy-preserving learning protocols
• Federated learning and distributed privacy-preserving algorithms
• Human-in-the-loop for privacy-aware machine learning
• Incentive mechanism and game theory
• Privacy aware knowledge driven federated learning
• Privacy-preserving techniques (secure multi-party computation, homomorphic encryption, secret sharing techniques, differential privacy) for machine learning
• Responsible, explainable and interpretability of AI
• Security for privacy
• Trade-off between privacy and efficiency

And with particular focuses but not limited to these application domains:

• Approaches to make AI GDPR-compliant
• Crowd intelligence
• Data value and economics of data federation
• Open-source frameworks for distributed learning
• Safety and security assessment of AI solutions
• Solutions to data security and small-data challenges in industries
• Standards of data privacy and security

• When: February 6, 2020
• Where: 1101 Kitchawan Road, Yorktown Heights, NY 10598
• Who: 12 invited professors and research scientists from IBM Research and WeBank and other institutions
• More: Questions? Email wuli@us.ibm.com

• Bijan Davari, IBM Research, USA (confirmed)
• Nathalie Baracaldo, IBM Research, USA (confirmed)
• Supriyo Chakraborty, IBM Research, USA (confirmed)
• Shiqiang Wang, IBM Research, USA (confirmed)
• Mikhail Yurochkin, IBM Research, USA (confirmed)
• Keith Bonawitz, Google Research, USA (confirmed)
• Ramesh Raskar, Massachusetts Institute of Technology, USA (confirmed)
• Qiang Yang, Webank, China (confirmed)
• Boi Faltings, Swiss Federal Institute of Technology Lausanne, Switzerland (confirmed)
• Mingyi Hong，University of Minnesota Twin Cities, USA(confirmed)

• Lingfei Wu (IBM Research AI, USA)
• Dinesh Verma (IBM Research, USA)
• Yang Liu (WeBank, China)

• Lingfei Wu, IBM Research AI, USA
• Shiqiang Wang, IBM Research, USA
• Supriyo Chakraborty, IBM Research, USA
• Shahrokh Daijavad, IBM Research, USA
• Dinesh Verma, IBM Research, USA
• Rania Khalaf, IBM Research, USA
• Liu Yang, WeBank, China
• Lixin Fan, WeBank, China
• Han Yu, Nanyang Technological University, Singapore