[Call for Papers]
Federated learning (FL) is one promising machine learning approach that trains a collective machine learning model using sharing data owned by various parties. It leverages many emerging privacy-reserving technologies (SMC, Homomorphic Encryption, differential privacy, etc.) to protect data owner privacy in FL. It has been gained popularity in some domains such as image classification, speech recognition, smart city, and healthcare. However, FL also faces multiple challenges that may potentially limit its applications in real-world use scenarios. For example, FL is still at the risk of various kinds of attacks that may result in leakage of individual data source privacy or degraded joint model accuracy. In other words, many existing FL solutions are still exposed to various security and privacy threats. This workshop aims to bring together FL researchers and practitioners to address the additional security and privacy threats and challenges in FL To make its mass adoption and widespread acceptance in the community. For example, privacy-specific threats in FL, training/inference phase attacks; data poisoning, model poisoning, how to handle Non-IID data without affecting the model performance, lacking trust from the FL participant, how to gain confidence by interpreting FL model, scheme of contributions and rewards to FL participants for improving an FL model, social and corporate responsibility towards the adoption of FL, imbalance data among FL participants, methods to verify and proof the correctness of FL computation, etc. The discussion in the workshop can lead implementing FL solutions that are more accurate, robust and interpretable, gain the trust of the FL participants.

Topics of interest include, but are not limited to:
- Interpretable Federated Learning
- Trade-Off between Privacy-Preserving and Explainable Federated Learning
- Federated Learning Multi-Party Computation
- Federated Learning Homomorphic Encryption
- Federated Learning Differential Privacy
- Federated Transfer Learning
- Federated Learning Personalization Techniques
- Federated Learning Attacks and Defenses
- Federated Learning Blockchain Network
- Federated Learning Secure Aggregation
- Federated Learning Fairness and Accuracy
- Federated Learning with Non-IID Data
- Federated Learning Incentive Mechanism
- Federated Learning Meets Mean-Field Game Theory
- Federated Learning-based Corporate Social Responsibility
- Social Responsible Federated Learning
- Decentralized Federated Learning
- Vertical Federated Learning

More information on previous workshops can be found here: http://federated-learning.org/

[Submission Instructions]
Each submission can be up to 9 pages including references. The submitted papers must be written in English and in PDF format according to the AAAI-22 template. All submitted papers will go through single-blinded peer review (i.e., author names and affiliations can be shown in the submissions) for their novelty, technical quality, impact, reproducibility, and so on. Submission will be accepted via the Easychair submission website.

Easychair submission site: https://easychair.org/conferences/?conf=fl-aaai-22

For enquiries, please email to: fl-aaai-22@easychair.org

[Publications]
Accepted papers will be invited to submit to a special issue of IEEE Transactions on Big Data.

[Organizing Committee]
Steering Chair:
- Qiang Yang (The Hong Kong University of Science and Technology / WeBank, China)
General Co-Chairs:
- Sin G. Teo (Institute for Infocomm Research, Singapore)
- Han Yu (Nanyang Technological University, Singapore)
- Lixin Fan (WeBank, China)
Program Co-Chairs:
- Chao Jin (Institute for Infocomm Research, Singapore)
- Le Zhang (University of Electronic Science and Technology, China)
- Yang Liu (Tsinghua University, China)
Publicity Co-Chairs:
- Zengxiang Li (Digital Research Institute, ENN Group, China)
- Xiuyi Fan (Nanyang Technological University, Singapore)
- Rui Lin (Chalmers University of Technology, Sweden)

[Program Committee]
- Ali Anwar (IBM)
- Alysa Ziying Tan (Alibaba-NTU Singapore Joint Research Institute)
- Anran Li (University of Science and Technology of China)
- Bing Luo (City University of Hong Kong, Shenzhen)
- Bingsheng He (National University of Singapore)
- Boi Faltings (École Polytechnique Fédérale de Lausanne)
- Chaoyang He (University of Southern California)
- Chuizheng Meng (University of Southern California)
- Di Chai (The Hong Kong University of Science and Technology)
- Dianbo Liu (Massachusetts Institute of Technology)
- Dimitrios Papadopoulos (The Hong Kong University of Science and Technology)
- Farzin Haddadpour (Yale University)
- Feng Yan (University of Nevada, Reno)
- Graham Cormode (The University of Warwick)
- Grigory Malinovsky (King Abdullah University of Science and Technology)
- Hongyi Wang (University of Wisconsin - Madison)
- Hongyuan Zhan (Facebook AI)
- Jiankai Sun (The Ohio State University)
- Jianshu Weng (AI Singapore)
- Jianyu Wang (Carnegie Mellon University)
- Jihong Park (Deakin University)
- Jinhyun So (University of Southern California)
- Jun Zhao (Nanyang Technological University)
- Junxue Zhang (The Hong Kong University of Science and Technology)
- Kallista Bonawitz (Google)
- Kevin Hsieh (Microsoft Research)
- Lei Jiao (University of Oregon)
- Lifeng Sun (Tsinghua University)
- Lingjuan Lyu (Sony AI)
- Mehrdad Mahdavi (The Pennsylvania State University)
- Mingyue Ji (University of Utah)
- Mingzhe Chen (Princeton University)
- Peng Zhang (Guangzhou University)
- Pengwei Xing (Nanyang Technological University)
- Praneeth Vepakomma (Massachusetts Institute of Technology)
- Rui Liu (Nanyang Technological University)
- Rui-Xiao Zhang (Tsinghua University)
- Samuel Horvath (King Abdullah University of Science and Technology)
- Sebastian Urban Stich (École Polytechnique Fédérale de Lausanne)
- Shangwei Guo (Chongqing University)
- Shiqiang Wang (IBM)
- Siwei Feng (Soochow University)
- Songze Li (The Hong Kong University of Science and Technology)
- Theodoros Salonidis (IBM)
- Tian Li (Carnegie Mellon University)
- Wei Yang Bryan Lim (Nanyang Technological university)
- Xiaohu Wu (Nanyang Technological University)
- Xiaoli Tang (Nanyang Technological University)
- Xu Guo (Nanyang Technological University)
- Yi Zhou (IBM Almaden Research Center)
- Yiyang Pei (Singapore Institute of Technology)
- Yuan Liu (Northeastern University)
- Yuang Jiang (Yale University)
- Yuxin Shi (Nanyang Technological University)
- Zehui Xiong (Singapore University of Technology and Design)
- Zelei Liu (Nanyang Technological University)
- Zheng Xu (Google)
- Zhuan Shi (University of Science and Technology of China)
- Zichen Chen (University of California, Santa Barbara)