Based on the survey that was sent earlier, we have 9 papers with authors interested in a virtual presentation. We will have a 90-minute virtual session of the workshop this Friday (Dec. 9) starting at 11 am EST / 4 pm UTC.

If you are interested in attending this virtual session, please fill in the following form by the end of Thursday (Dec. 8): https://forms.gle/a432U639Sx2sn7Xe8. We will send meeting info to the email address that you include in the form before the virtual session starts on Friday.

• Outstanding Paper Award: Jayanth Reddy Regatti, Songtao Lu, Abhishek Gupta and Ness Shroff. Conditional Moment Alignment for Improved Generalization in Federated Learning
• Outstanding Paper Award: Sai Praneeth Karimireddy, Wenshuo Guo and Michael Jordan. Mechanisms that Incentivize Data Sharing in Federated Learning

1. Jayanth Reddy Regatti, Songtao Lu, Abhishek Gupta and Ness Shroff. Conditional Moment Alignment for Improved Generalization in Federated Learning
2. Sai Praneeth Karimireddy, Wenshuo Guo and Michael Jordan. Mechanisms that Incentivize Data Sharing in Federated Learning
3. Hanhan Zhou, Tian Lan, Guru Prasadh Venkataramani and Wenbo Ding. Federated Learning with Online Adaptive Heterogeneous Local Models
4. Baturalp Buyukates, Jinhyun So, Hessam Mahdavifar and Salman Avestimehr. LightVeriFL: Lightweight and Verifiable Secure Federated Learning
5. Francesco Pase, Berivan Isik, Deniz Gunduz, Tsachy Weissman and Michele Zorzi. Efficient Federated Random Subnetwork Training
6. Filippo Galli, Sayan Biswas, Gangsoo Zeong, Tommaso Cucinotta and Catuscia Palamidessi. Group privacy for personalized federated learning
7. Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith and Gauri Joshi. To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning
8. Marco Bornstein, Tahseen Rabbani, Evan Wang, Amrit Bedi and Furong Huang. SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
9. Sharut Gupta, Kartik Ahuja, Mohammad Havaei, Niladri Chatterjee and Yoshua Bengio. FL Games: A Federated Learning Framework for Distribution Shifts
10. Simone Bottoni, Giulio Zizzo, Stefano Braghin and Alberto Trombetta. Verifiable Federated Machine Learning
11. Yeojoon Youn, Bhuvesh Kumar and Jacob Abernethy. Accelerated Federated Optimization with Quantization
12. Xingchen Ma, Junyi Zhu and Matthew Blaschko. Tackling Personalized Federated Learning with Label Concept Drift via Hierarchical Bayesian Modeling

1. Jaeheon Kim and Bong Jun Choi. FedTH : Tree-based Hierarchical Image Classification in Federated Learning
2. M. Taha Toghani and Cesar Uribe. Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation
3. Khaoula Chehbouni, Gilles Caporossi, Reihaneh Rabbany, Martine De Cock and Golnoosh Farnadi. Early Detection of Sexual Predators with Federated Learning
4. Timothy Castiglia, Shiqiang Wang and Stacy Patterson. Self-Supervised Vertical Federated Learning
5. Pei Fang and Jinghui Chen. On the Vulnerability of Backdoor Defenses for Federated Learning
6. Mariel AF Werner, Lie He, Sai Praneeth Karimireddy, Michael Jordan and Martin Jaggi. Towards Provably Personalized Federated Learning via Threshold-Clustering of Similar Clients
7. Xinwei Zhang, Bingqing Song, Mehrdad Honarkhah, Jie Ding and Mingyi Hong. Building Large Machine Learning Models from Small Distributed Models: A Layer Matching Approach
8. Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga, Masoud Faraki, Manmohan Chandraker and Yu-Xiang Wang. Voting-Based Approaches for Differentially Private Federated Learning
9. Md Ibrahim Ibne Alam, Koushik Kar, Theodoros Salonidis and Horst Samulowitz. DASH: Decentralized CASH for Federated Learning
10. Yujia Wang, Pei Fang and Jinghui Chen. Accelerating Adaptive Federated Optimization with Local Gossip Communications
11. Dimitris Stripelis, Umang Gupta, Greg Ver Steeg and Jose Luis Ambite. Federated Progressive Sparsification (Purge-Merge-Tune)+
12. Pedro Valdeira, Yuejie Chi, Claudia Soares and Joao Xavier. A Multi-Token Coordinate Descent Method for Vertical Federated Learning
13. Rajarshi Saha, Michal Yemini, Emre Ozfatura, Deniz Gunduz and Andrea Goldsmith. ColRel: Collaborative Relaying for Federated Learning over Intermittently Connected Networks
14. Ziwei Li, Hong-You Chen, Han Wei Shen and Wei-Lun Chao. Understanding Federated Learning through Loss Landscape Visualizations: A Pilot Study
15. Krishna Pillutla, Yassine Laguel, Jérôme Malick and Zaid Harchaoui. Differentially Private Federated Quantiles with the Distributed Discrete Gaussian Mechanism
16. Chen Dun, Mirian Hipolito Garcia, Dimitrios Dimitriadis, Christopher Jermaine and Anastasios Kyrillidis. Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
17. Junyi Li and Heng Huang. FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings
18. Ljubomir Rokvic, Panayiotis Danassis and Boi Faltings. Privacy-Preserving Data Filtering in Federated Learning Using Influence Approximation
19. John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi and Mike Rabbat. Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
20. Joseph Lavond, Minhao Cheng and Yao Li. Trusted Aggregation (TAG): Model Filtering Backdoor Defense In Federated Learning
21. Stefanos Laskaridis, Javier Fernandez-Marques and Łukasz Dudziak. Cross-device Federated Architecture Search
22. Parker Newton, Olivia Choudhury, Bill Horne, Vidya Ravipati, Divya Bhargavi and Ujjwal Ratan. Client-Private Secure Aggregation for Privacy-Preserving Federated Learning
23. Shanshan Wu, Tian Li, Zachary Charles, Yu Xiao, Ken Liu, Zheng Xu and Virginia Smith. Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
24. Daniel Lopes, João Nadkarni, Filipe Assunção, Miguel Lopes and Luís Rodrigues. Federated Learning for Predicting the Next Node in Action Flows
25. Chuan Guo, Kamalika Chaudhuri, Pierre Stock and Mike Rabbat. The Interpolated MVU Mechanism For Communication-efficient Private Federated Learning
26. Yi Sui, Junfeng Wen, Yenson Lau, Brendan Ross and Jesse Cresswell. Find Your Friends: Personalized Federated Learning with the Right Collaborators
27. Saeed Vahidian, Mahdi Morafah, Chen Chen, Mubarak Shah and Bill Lin. Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks
28. Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro and Miguel Rodrigues. Federated Fairness without Access to Demographics
29. Mirian Hipolito Garcia, Andre Manoel, Daniel Madrigal, Robert Sim and Dimitrios Dimitriadis. FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
30. Aleksei Triastcyn, Matthias Reisser and Christos Louizos. Decentralized Learning with Random Walks and Communication-Efficient Adaptive Optimization
31. Parsa Assadi, Byung Hoon Ahn and Hadi Esmaeilzadeh. Accelerating Federated Learning Through Attention on Local Model Updates
32. Ali Dadras, Karthik Prakhya and Alp Yurtsever. Federated Frank-Wolfe Algorithm
33. Atahan Ozer, Kadir Burak Buldu, Abdullah Akgül and Gozde Unal. How to Combine Variational Bayesian Networks in Federated Learning
34. Chhavi Sharma, Vishnu Narayanan and Balamurugan Palaniappan. Stochastic Gradient Methods with Compressed Communication for Decentralized Saddle Point Problems
35. Batiste Le bars, Aurélien Bellet, Marc Tommasi, Erick Lavoie and Anne-marie Kermarrec. Refined Convergence and Topology Learning for Decentralized Optimization with Heterogeneous Data
36. Amr Abourayya, Michael Kamp, Erman Ayday, Jens Kleesiek, Kanishka Rao, Geoffrey Webb and Bharat Rao. AIMHI: Protecting Sensitive Data through Federated Co-Training
37. Ilias Driouich, Chuan Xu, Giovanni Neglia, Frederic Giroire and Eoin Thomas. A Novel Model-Based Attribute Inference Attack in Federated Learning
38. Athul Sreemathy Raj, Irene Tenison, Kacem Khaled, Felipe Gohring de Magalhães and Gabriela Nicolescu. FedSHIBU: Federated Similarity-based Head Independent Body Update
39. Jaewoo Shin, Taehyeon Kim and Se-Young Yun. Revisiting the Activation Function for Federated Image Classification
40. Zhaozhuo Xu, Luyang Liu, Zheng Xu and Anshumali Shrivastava. Adaptive Sparse Federated Learning in Large Output Spaces via Hashing
41. Holger R Roth, Yan Cheng, Yuhong Wen, Isaac Yang, Ziyue Xu, YuanTing Hsieh, Kristopher Kersten, Ahmed Harouni, Can Zhao, Kevin Lu, Zhihong Zhang, Wenqi Li, Andriy Myronenko, Dong Yang, Sean Yang, Nicola Rieke, Abood Quraini, Chester Chen, Daguang Xu, Nic Ma, Prerna Dogra, Mona G Flores and Andrew Feng. FLARE: Federated Learning from Simulation to Real-World
42. Liam Collins, Enmao Diao, Tanya Roosta, Jie Ding and Tao Zhang. PerFedSI: A Framework for Personalized Federated Learning with Side Information
43. Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu and Yue Liu. FedSynth: Gradient Compression via Synthetic Data in Federated Learning
44. Sourasekhar Banerjee, Alp Yurtsever and Monowar H Bhuyan. Personalized Multi-tier Federated Learning
45. Saeed Vahidian, Mahdi Morafah, Weijia Wang and Bill Lin. FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution
46. Hamid Mozaffari, Virendra Marathe and Dave Dice. Private and Robust Federated Learning using Private Information Retrieval and Norm Bounding
47. Karthik Prasad, Sayan Ghosh, Graham Cormode, Ilya Mironov, Ashkan Yousefpour and Pierre Stock. Reconciling Security and Communication Efficiency in Federated Learning
48. Shashi Raj Pandey, Lam Nguyen and Petar Popovski. FedToken: Tokenized Incentives for Data Contribution in Federated Learning
49. Giulio Zizzo, Ambrish Rawat, Naoise Holohan and Seshu Tirupathi. Federated Continual Learning with Differentially Private Data Sharing
50. Zhiwei Tang, Yanmeng Wang and Tsung-Hui Chang. z-SignFedAvg: A unified sign-based stochastic compression for federated learning
51. Kiwan Maeng, Chuan Guo, Sanjay Kariyappa and Edward Suh. Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information
52. Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu and Cho-Jui Hsieh. FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
53. Haibo Yang, Peiwen Qiu, Prashant Khanduri and Jia Liu. With a Little Help from My Friend: Server-Aided Federated Learning with Partial Client Participation
54. Yue Niu, Saurav Prakash, Souvik Kundu, Sunwoo Lee and Salman Avestimehr. Federated Learning of Large Models at the Edge via Principal Sub-Model Training
55. Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng, Lin Chen, Carlee Joe-Wong and Tianqiang Liu. FedRule: Federated Rule Recommendation System with Graph Neural Networks
56. Chulin Xie, Pin-Yu Chen, Ce Zhang and Bo Li. Improving Vertical Federated Learning by Efficient Communication with ADMM
57. Virendra Marathe, Pallika Kanani and Daniel W. Peterson. Subject Level Differential Privacy with Hierarchical Gradient Averaging
58. Jingtao Li, Lingjuan Lyu, Daisuke Iso, Chaitali Chakrabarti and Michael Spranger. MocoSFL: Enabling Cross-Client Collaborative Self-Supervised Learning
59. Marco Schreyer, Hamed Hemati, Damian Borth and Miklos A. Vasarhelyi. Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
60. Motasem Alfarra, Juan Camilo Perez, Egor Shulgin, Peter Richtárik and Bernard Ghanem. Certified Robustness in Federated Learning
61. Sara Babakniya, Souvik Kundu, Saurav Prakash, Yue Niu and Salman Avestimehr. Federated Sparse Training: Lottery Aware Model Compression for Resource Constrained Edge
62. Mathieu Even, Hadrien Hendrikx and Laurent Massoulié. Asynchronous Speedup in Decentralized Optimization

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 FL, 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, while noting that FL has become an increasingly popular topic in the machine learning community in recent years.

Topics of interest include, but are not limited to, the following:

Adversarial attacks on FL Applications of FL Blockchain for FL Beyond first-order methods in FL Beyond local methods in FL Communication compression in FL Data heterogeneity in FL Decentralized FL Device heterogeneity in FL

Fairness in FL Hardware for on-device FL Variants of FL like split learning Local methods in FL Nonconvex FL Operational challenges in FL Optimization advances in FL Partial participation in FL

Personalization in FL Privacy concerns in FL Privacy-preserving methods for FL Resource-efficient FL Systems and infrastructure for FL Theoretical contributions to FL Uncertainty in FL Vertical FL

The workshop will have invited talks on a diverse set of topics related to FL. In addition, we plan to have an industrial panel and booth, where researchers from industry will talk about challenges and solutions from an industrial perspective.

More information on previous workshops can be found here.

Submissions should be no more than 6 pages long, excluding references, and follow NeurIPS'22 template. Submissions are double-blind (author identity shall not be revealed to the reviewers), so the submitted PDF file should not include any identifiable information of authors. An optional appendix of any length is allowed and should be put at the end of the paper (after references).

Submissions are collected on OpenReview at the following link: https://openreview.net/group?id=NeurIPS.cc/2022/Workshop/Federated_Learning.
Accepted papers and their review comments will be posted on OpenReview in public. Due to the short timeline, we will not have a rebuttal period, but the authors are encouraged to interact and discuss with reviewers on OpenReview after the acceptance notifications are sent out. Rejected papers and their reviews will remain private and not posted in public.

For questions, please contact: fl-neurips-2022@googlegroups.com

Our workshop does not have formal proceedings, i.e., it is non-archival. Accepted papers will be available in public on OpenReview together with the reviewers' comments. Revisions to accepted papers will be allowed until shortly before the workshop date.

We welcome submissions of unpublished papers, including those that are submitted to other venues if that other venue allows so. However, papers that have been accepted to an archival venue as of Sept. 21, 2022 should not be resubmitted to this workshop, because the goal of the workshop is to share recent results and discuss open problems. Specifically, papers that have been accepted to NeurIPS'22 main conference should not be resubmitted to this workshop.

The workshop will primarily take place physically with in person attendance. For presenters who cannot attend in person, it is planned to be made possible to connect remotely over Zoom for the oral talks. However, the poster sessions will be in-person only. Depending on the situation, we may include a lightening talk session for accepted poster presentations where the presenters cannot attend physically, or organize a separate virtual session after the official workshop date. If a paper is accepted as an oral talk, the NeurIPS organizers require a pre-recording of the presentation by early November, which will be made available for virtual participants to view. All accepted papers will be posted on OpenReview and linked on our webpage.

• Nathalie Baracaldo (IBM Research Almaden, USA)
• Olivia Choudhury (Amazon, USA)
• Gauri Joshi (Carnegie Mellon University, USA)
• Peter Richtárik (King Abdullah University of Science and Technology, Saudi Arabia)
• Praneeth Vepakomma (Massachusetts Institute of Technology, USA)
• Shiqiang Wang (IBM T. J. Watson Research Center, USA)
• Han Yu (Nanyang Technological University, Singapore)