Day 1 - June 17th

Room: SEA-PKV-2-Center of the Universe

8:00 - 9:00 AM Registration/Breakfast

9:00 - 9:30 AM Daniel Ramage and Brendan McMahan - Welcome & Overview

9:30 - 10:30 AM Lightning Talks (Session Chair: Peter Kairouz)

Adria Gascon, Distributed Summation in the Shuffle Model of Differential Privacy

Borja Balle, Privacy Amplification by Mixing

Aurélien Bellet, Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs

Gauri Joshi, Adaptive Communication Strategies for Local-Update SGD

Salim El Rouayheb, Single Server PIR: A Song of Computation & Information

Han Yu, Incentive Mechanism Design for Federated Learning

Sanmi Koyejo, Asynchrony and Fault-tolerance in Federated ML; Two Vignettes

Justin Hsu, Data Poisoning against Differentially-Private Learners: Attacks and Defenses

Richard Nock, On Noisy Record Linkage and Learning from Distributed Data

Andres Munoz Medina, Differentially Private Covariance Estimation

Ananda Theertha Suresh / Mehryar Mohri, Agnostic Federated Learning

Jayadev Acharya, Distributed Inference Under Information Constraints: Communication, Privacy, and the Role of Shared Randomness

10:30 -11:00 AM Coffee Break

11:00 - 12:15 PM Session 1: Federated Learning and Analytics (Session Chair: Jakub Konecny)

Françoise Beaufays, Gboard: a Showcase for Federated Learning

Phillip Gibbons, Geo-distributed Learning: The Non-IID Quagmire

Martin Jaggi, Decentralized Training with Compressed Communication

Ayfer Ozgur, A Statistical Estimation Approach to Federated Learning

12:15 - 1:45 PM Lunch & Poster Session

1:45 - 2:15 PM Krzysztof Ostrowski - Using TensorFlow Federated for Research

Nati Srebro - Federated Learning: an Optimization Perspective

2:15 - 3:30 PM Session 2: Privacy, Security, and Fairness I (Session Chair: Keith Bonawitz)

Graham Cormode, Local Differential Privacy: Solution or Distraction?

David Evans, Meaningful Privacy for Federated Learning

Aleksandra Korolova, Hybrid Trust models for Democratizing Differential Privacy

Raef Bassily, Private Stochastic Convex Optimization with Optimal Rate

3:30 - 4:00 PM Coffee Break

4:00 - 5:30 PM Breakout Sessions

B1: Making FL & FA more efficient and effective

B2: Expanding the reach of FL to more devices and more problems

B3: Relaxing the core FL assumptions - applications to new settings and scenarios

B4: Robustness, Attacks, and Defenses

B5. Objectives beyond accuracy and utility: Privacy, Security, and Fairness

5:30 - 6:00 PM University Relations Talk

6:00 - 9:00 PM Argosy Dinner and Cruise

Day 2 - June 18th

Room: SEA-PKV-2-Center of the Universe

8:00 - 8:30 AM Registration/breakfast

8:30 - 9:00 AM Keith Bonawitz - Shaping Information, Building for Trust

9:00 - 10:15 AM Session 3: Privacy, Security, and Fairness II (Session Chair: Kunal Talwar)

Ben Hutchinson, Fairness in ML: Challenges and Opportunities for Federated Learning

Li Xiong, Collaborative Aggregation and Analytics with Differential Privacy

Kareem Amin, Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy

Naman Agarwal, cpSGD: Communication-efficient and differentially-private distributed SGD

10:15 -10:45 AM Coffee Break

10:45 - 12:00PM Session 4: Robustness, Attacks, and Defenses (Session Chair: Stefano Mazzocchi)

Prateek Mittal, Analyzing Federated Learning through an Adversarial Lens

Emiliano De Cristofaro, Exploiting Unintended Feature Leakage in Collaborative Learning

Zaid Harchaoui, Robust and Secure Aggregation for Federated Learning

Vitaly Shmatikov, Integrity Threats to Federated Learning and How to Mitigate Them

12:00 - 1:00 PM Lunch

1:00 - 1:30 PM Stefano Mazzocchi, Peter Kairouz, and Sean Augenstein - Federated Beyond Learning

1:30 - 3:00 PM Session 5: Relaxing the Core Assumptions of Federated Learning (Session Chair: Sean Augenstein)

Ramesh Raskar, Split Learning: A federated learning approach to thin clients

Farinaz Koushanfar / Tara Javidi, Peer to Peer Federated Bayesian Learning

Mehdi Bennis, Wireless Network Intelligence at the Edge

Dawn Song, Decentralized Federated Learning and its Application for Anomaly Detection

Qiang Yang, Federated Transfer Learning

3:00 - 3:30 PM Coffee Break

3:30 - 4:30 PM Editorial Breakout Sessions

B1: Making FL & FA more efficient and effective

B2: Expanding the reach of FL to more devices and more problems

B3: Relaxing the core FL assumptions - applications to new settings and scenarios

B4: Robustness, Attacks, and Defenses

B5: Objectives beyond accuracy and utility: Privacy, Security, and Fairness

4:30 - 5:00 PM Concluding Note and Q&A