Accepted Full Papers
1. Laura Rieger, Rasmus Malik Thaarup Høegh and Lars Kai Hansen. Client Adaptation improves Federated Learning with Simulated Non-IID Clients
2. Jinhyun So, Basak Guler and A. Salman Avestimehr. Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
3. Wonyong Jeong, Jaehong Yoon, Eunho Yang and Sung Ju Hwang. Federated Semi-Supervised Learning with Inter-Client Consistency
4. Ishika Singh, Haoyi Zhou, Kunlin Yang, Meng Ding, Bill Lin and Pengtao Xie. Differentially-private Federated Neural Architecture Search
5. Chong Liu, Yuqing Zhu, Kamalika Chaudhuri and Yu-Xiang Wang. Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
6. Honglin Yuan and Tengyu Ma. Federated Accelerated Stochastic Gradient Descent
7. Krishna Pillutla, Sham Kakade and Zaid Harchaoui. Robust Aggregation for Federated Learning
8. Leighton Pate Barnes, Huseyin A. Inan, Berivan Isik and Ayfer Ozgur. rTop-k: A Statistical Estimation Approach to Distributed SGD
9. Ashkan Yousefpour, Brian Nguyen, Siddartha Devic, Guanhua Wang, Abdul Rahman Kreidieh, Hans Lobel, Alexandre Bayen and Jason Jue. ResiliNet: Failure-Resilient Inference in Distributed Neural Networks
10. Hanlin Lu, Changchang Liu, Ting He, Shiqiang Wang and Kevin S. Chan. Sharing Models or Coresets: A Study based on Membership Inference Attack
11. Swanand Kadhe, Nived Rajaraman, O. Ozan Koyluoglu and Kannan Ramchandran. FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning

Accepted Short Papers
1. Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, Mohammad Shikh-Bahaei, H. Vincent Poor and Shuguang Cui. Delay Minimization for Federated Learning Over Wireless Communication Networks
2. Angel Navia Vázquez, Manuel-Alberto Vázquez-López and Jesús Cid-Sueiro. Double Confidential Federated Machine Learning Logistic Regression for Industrial Data Platforms
3. Kun Li, Fanglan Zheng, Jiang Tian and Xiaojia Xiang. A Federated F-score Based Ensemble Model for Automatic Rule Extraction
4. Hajime Ono and Tsubasa Takahashi. Locally Private Distributed Reinforcement Learning
5. Avishek Ghosh, Jichan Chung, Dong Yin and Kannan Ramchandran. An Efficient Framework for Clustered Federated Learning
6. Yang Liu, Zhihao Yi and Tianjian Chen. Defending backdoor attacks in feature-partitioned collaborative learning
7. Tianyi Chen, Xiao Jin, Yuejiao Sun and Wotao Yin. VAFL: a Method of Vertical Asynchronous Federated Learning
8. Shahab Asoodeh and Flavio Calmon. Differentially Private Federated Learning: An Information-Theoretic Perspective
9. Mathieu Andreux, Andre Manoel, Romuald Menuet, Charlie Saillard and Chloé Simpson. Federated Survival Analysis with Discrete-Time Cox Models
10. Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz, Amir Salman Avestimehr and Nageen Himayat. Coded Computing for Federated Learning at the Edge
11. Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani and Ali Jadbabaie. Robust Federated Learning: The Case of Affine Distribution Shifts
12. Mikhail Khodak, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith and Ameet Talwalkar. Weight-Sharing for Hyperparameter Optimization in Federated Learning
13. Vaikkunth Mugunthan, Ravi Rahman and Lalana Kagal. BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
14. Vaikkunth Mugunthan, Anton Peraire-Bueno and Lalana Kagal. PrivacyFL: A simulator for privacy-preserving and secure federated learning
15. Myungjae Shin, Chihoon Hwang, Joongheon Kim, Jihong Park, Mehdi Bennis and Seong-Lyun Kim. XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
16. Hossein Hosseini, Sungrack Yun, Hyunsin Park, Christos Louizos, Joseph Soriaga and Max Welling. Federated Learning of User Authentication Models
17. Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin and Yang Liu. FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data

Call for Papers
Training machine learning models in a centralized fashion often faces significant challenges due to regulatory and privacy concerns in real-world use cases. These include distributed training data, computational resources to create and maintain a central data repository, and regulatory guidelines (GDPR, HIPAA) that restrict sharing sensitive data. Federated learning (FL) is a new paradigm in machine learning that can mitigate these challenges by training a global model using distributed data, without the need for data sharing. The extensive application of machine learning to analyze and draw insight from real-world, distributed, and sensitive data necessitates familiarization with and adoption of this relevant and timely topic among the scientific community.
Despite the advantages of federated learning, and its successful application in certain industry-based cases, this field is still in its infancy due to new challenges that are imposed by limited visibility of the training data, potential lack of trust among participants training a single model, potential privacy inferences, and in some cases, limited or unreliable connectivity.

The goal of this workshop is to bring together researchers and practitioners interested in FL. This day-long event will facilitate interaction among students, scholars, and industry professionals from around the world to understand the topic, identify technical challenges, and discuss potential solutions. This will lead to an overall advancement of FL and its impact in the community. Topics of interest include, but are not limited to, the following:

- Adversarial attacks on FL
- Blockchain for FL
- Fairness in FL
- Hardware for on-device FL
- Novel applications of FL
- Operational challenges in FL
- Personalization in FL
- Privacy concerns in FL
- Privacy-preserving methods for FL
- Resource-efficient FL
- System and infrastructure for FL
- Theoretical contributions to FL
- Uncertainty in FL
- Submission Instructions

Proceedings and Dual Submission Policy
Our workshop has no formal proceedings. Accepted papers will be posted on the workshop webpage. We welcome submissions of unpublished papers, including those that are submitted/accepted to other venues if that other venue allows so. We will not accept papers that are already published though, because the goal of the workshop is to share recent results and discuss open problems.

Submission Instructions
Submissions must be at most 6 pages long, excluding references, and follow ICML-20 template. Submissions are single-blind and author identity will be revealed to the reviewers. An optional appendix of arbitrary length is allowed and should be put at the end of the paper (after references).

Easychair submission link: https://easychair.org/conferences/?conf=flicml20

If you have any enquiries, please email us at: flworkshop.icml.2020@gmail.com

Organizing Committee
- Nathalie Baracaldo (IBM Research Almaden, USA)
- Olivia Choudhury (IBM Research Cambridge, USA)
- Gauri Joshi (Carnegie Mellon University, USA)
- Ramesh Raskar (MIT Media Lab, USA)
- Shiqiang Wang (IBM T. J. Watson Research Center, USA)
- Han Yu (Nanyang Technological University, Singapore)

Program Committee
- M. Hadi Amini (Carnegie Mellon University, USA)
- Mehdi Bennis (University of Oulu, Finland)
- Supriyo Chakraborty (IBM Research, USA)
- Mingzhe Chen (Princeton University, USA)
- Boi Faltings (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
- Mingyi Hong (University of Minnesota, USA)
- Mingyue Ji (University of Utah, USA)
- Peter Kairouz (Google AI, USA)
- Jakub Konečný (Google, USA)
- Kin K. Leung (Imperial College, UK)
- Dianbo Liu (Massachusetts Institute of Technology, USA)
- Yang Liu (Webank, China)
- Ji Liu (Stony Brook University, USA)
- Changchang Liu (IBM Research, USA)
- Mehrdad Mahdavi (Pennsylvania State University, USA)
- Kshitiz Malik (Facebook, USA)
- Jihong Park (Deakin University, Australia)
- Stacy Patterson (Rensselaer Polytechnic Institute)
- Peter Richtarik (King Abdullah University of Science and Technology, Saudi Arabia)
- Shahin Shahrampour (Texas A&M University, USA)
- Sebastian Urban Stich (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
- Andrew Trask (DeepMind, USA)
- Praneeth Vepakomma (Massachusetts Institute of Technology, USA)
- Lingfei Wu (IBM Research AI, USA)
- Poonam Yadav (University of York, UK)
- Ashkan Yousefpour (Facebook AI, USA)
- Mikhail Yurochkin (IBM Research, USA)
- Hongyuan Zhan (Facebook, USA)