• Best Paper: Chaoyang He, Zhengyu Yang, Erum Mushtaq, Sunwoo Lee, Mahdi Soltanolkotabi and Salman Avestimehr. SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
• Best Student Paper: Chen Chen, Jie Zhang and Lingjuan Lyu. GEAR: A Margin-based Federated Adversarial Training Approach
• Best Application Paper: Chengyi Yang, Jia Liu, Hao Sun, Tongzhi Li and Zengxiang Li. WT-Shapley: Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection

1. Chen Chen, Jie Zhang and Lingjuan Lyu. GEAR: A Margin-based Federated Adversarial Training Approach
2. Chengyi Yang, Jia Liu, Hao Sun, Tongzhi Li and Zengxiang Li. WT-Shapley: Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection
3. Chaoyang He, Zhengyu Yang, Erum Mushtaq, Sunwoo Lee, Mahdi Soltanolkotabi and Salman Avestimehr. SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
4. Sunwoo Lee, Anit Sahu, Chaoyang He and Salman Avestimehr. Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits
5. Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang, Di Fan, Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi and Salman Avestimehr. FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
6. Chen Chen, Lingjuan Lyu, Yuchen Liu, Fangzhao Wu, Chaochao Chen and Gang Chen. Byzantine-resilient Federated Learning via Gradient Memorization
7. Jiahui Geng, Yongli Mou, Feifei Li, Qing Li, Oya Beyan, Stefan Decker and Chunming Rong. Towards General Deep Leakage in Federated Learning
8. Shraman Pal, Mansi Uniyal, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Moongu Jeon and Jinho Choi. Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
9. Jiyue Huang, Chi Hong, Yang Liu, Lydia Y. Chen and Stefanie Roos. Tackling Mavericks in Federated Learning via Adaptive Client Selection Strategy
10. Yuezhou Wu, Yan Kang, Jiahuan Luo, Yuanqin He and Qiang Yang. FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
11. Hangrui Cao, Qiying Pan, Yifei Zhu and Jiangchuan Liu. Birds of a Feather Help: Context-aware Client Selection for Federated Learning
12. Yuchen Zeng, Hongxu Chen and Kangwook Lee. Improving Fairness via Federated Learning
13. Yuting He, Yiqiang Chen, Xiaodong Yang, Yingwei Zhang and Bixiao Zeng. Class-Wise Adaptive Self Distillation for Heterogeneous Federated Learning
14. Jichan Chung, Kangwook Lee and Kannan Ramchandran. Federated Unsupervised Clustering with Generative Models
15. Haizhou Shi, Youcai Zhang, Zijin Shen, Siliang Tang, Yaqian Li, Yandong Guo and Yueting Zhuang. Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning
16. Hamid Mozaffari, Virat Shejwalkar and Amir Houmansadr. Robust Federated Learning By Training on Parameter Ranks
17. Yujia Wang, Lu Lin and Jinghui Chen. Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization
18. Tahseen Rabbani, Brandon Feng, Yifan Yang, Arjun Rajkumar, Amitabh Varshney and Furong Huang. Comfetch: Federated Learning of Large Networks on Memory-Constrained Clients via Sketching

1. Jingoo Han, Ahmad Faraz Khan, Syed Zawad, Ali Anwar, Nathalie Baracaldo Angel, Yi Zhou, Feng Yan and Ali R. Butt. Tokenized Incentive for Federated Learning
2. Morris Stallmann and Anna Wilbik. Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)
3. Chao Jin, Jun Wang, Sin Gee Teo, Le Zhang, Cs Chan, Qibin Hou and Khin Mi Mi Aung. End-to-End Secure and Efficient Federated Learning for XGBoost
4. K. R. Jayaram, Vinod Muthusamy, Gegi Thomas, Ashish Verma and Mark Purcell. Lambda FL: Serverless Aggregation For Federated Learning
5. Jinhyun So, Ramy E. Ali, Basak Guler, Jiantao Jiao and Salman Avestimehr. Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
6. Ninareh Mehrabi, Cyprien de Lichy, John McKay, Cynthia He and William Campbell. Towards Multi-Objective Statistically Fair Federated Learning [Poster]
7. Erum Mushtaq, Chaoyang He, Jie Ding and Salman Avestimehr. SPIDER: Searching Personalized Neural Architecture for Federated Learning
8. Afra Mashhadi, Alex Kyllo and Reza Parizi. Fairness in Federated Learning for Spatial-Temporal Applications
9. Meijie Wen and Shaofu Yang. Decentralized Stochastic Optimization with Fixed-Time Computation and Compressed Communication
10. Wenqing Zhang, Yang Qiu, Song Bai, Rui Zhang, Xiaolin Wei and Xiang Bai. FedOCR: Efficient and Secure Federated Learning for Scene Text Recognition
11. Charles Lu and Jayashree Kalpathy-Cramer. Distribution-Free Federated Learning with Conformal Predictions
12. Lianlian Jiang, Yuexuan Wang, Wenyi Zheng, Chao Jin, Zengxiang Li and Sin G. Teo. LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data
13. Haoran Shi, Yali Jiang, Han Yu, Yonghui Xu and Lizhen Cui. MVFLS: Multi-participant Vertical Federated Learning based on Secret Sharing
14. Zexi Yao, Chao Jin, Mohamed Ragab, Khin Mi Mi Aung and Xiaoli Li. DiagNet: Machine Fault Diagnosis Using Federated Transfer Learning in Low Data Regimes
15. Khouloud Abdelli, Joo Yeon Cho and Stephan Pachnicke. Secure and Robust Federated Learning for Predictive Maintenance in Optical Networks
16. Balázs Pejó and Gergely Biczok. Quality Inference in Federated Learning with Secure Aggregation [Poster]
17. Riadh Ben Chaabene, Darine Ameyed and Mohamed Cheriet. Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance
18. Hyunsu Mun and Youngseok Lee. FLHub: a Federated Learning model sharing service
19. Sheng Guo, Zengxiang Li, Hui Liu, Shubao Zhao and Cheng Hao Jin. Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery

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.

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 be under a single-blinded peer review 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

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

• 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)