The organizers and ICLR workshop chairs have worked together to find a room for in-person workshop. A huge thanks to Andrea Brown for supporting us. We encourage in-person participation while will try our best to provide accessibility. This workshop is accepted to ICLR as virtual. We will update with more information later, and please make your travel plan accordingly.

Important Dates

• Submission Due Date: February 4th February 9th, 2024, AoE
• Notification of Acceptance: March 3rd, 2024, AoE
• Camera-ready Papers Due: May 3rd, 2024, AoE
• Workshop Date: Saturday, May 11th, 2024, Vienna, Austria

Submission Instructions

Submissions should be double-blind, no more than 6 pages long (excluding references), and following the ICLR 2024 template. An optional appendix of any length can be put at the end of the draft (after references).

Submissions are processed in OpenReview. https://openreview.net/group?id=ICLR.cc/2024/Workshop/PML.

Our workshop does not have formal proceedings, i.e., it is non-archival. Accepted papers and their review comments will be posted on OpenReview in public (after the end of the review process), while rejected and withdrawn papers and their reviews will remain private.

We welcome submissions from novel research, ongoing (incomplete) projects, drafts currently under review at other venues, as well as recently published results. However, we request significant updates if the work has previously been presented at major machine learning conferences or workshops before May 1st, 2024.

Camera Ready Instructions

Please keep using the ICLR template for camera ready, and feel free to update the footnote/header in the template from ICLR main conference to workshop.

We allow an extra page (7 pages for main context) to incorporate reviewer feedback, and adding additional information such as authors formatting, limitations, ethics and acknowledgement.

The accepted papers will become public on openreview after the camera ready dealine. The reviews will **not** be publicized.

Presentation Instructions

We encourage in-person presentations, while also aim to provide accessibility to virtual attendees.

In-person posters should be portrait orientation and up to 24"w x 36"h (W 60.96 x H 91.44 cm) size.

For every accepted paper, authors can optinoally submit a virtual poster and a 5-min recorded video (links are preferred, e.g., Google Drive, YouTube etc.) to be highlighted on our webpage. Please email to privacy-workshop-iclr24@googlegroups.com with "[PML-ICLR24 Virtual Presentation]+ Paper Title" as email title.

Best Paper Awards

The organizing committee will select best paper award(s) supported by our sponsors.

Early Career Free Registration

The workshop can provide limited number of free (full ICLR'24 conference) registration to our attendees, which will prioritize early career students, and promote diversity, equity and inclusion (DEI). If you are interested, please email us at privacy-workshop-iclr24@googlegroups.com following the instructions:

• Email has to be sent before Apr 20th to be considered.
• Email title starts with [PML-ICLR24 free registration].
• Includes link(s) to your accepted, or submitted paper(s) to our workshop.
• Indicates whether you will attend the workshop in-person.
• Includes a short paragraph describing why it is important for your research and career.
• (Optional) includes link(s) to your webpage and resume.
• The awardees will be announced in late April. Registration fee can be refunded (handled by ICLR) if you already registered.

Spotlight Presentations

Morning Session

• Efficient Language Model Architectures for Differentially Private Federated Learning. Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh.
• DNA: Differential privacy Neural Augmentation for contact tracing. Rob Romijnders, Christos Louizos, Yuki M Asano, Max Welling.
• PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. Charlie Hou, Akshat Shrivastava, Hongyuan Zhan, Rylan Conway, Trang Le, Adithya Sagar, Giulia Fanti, Daniel Lazar.
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest. Basileal Yoseph Imana, Aleksandra Korolova, John Heidemann.

Afternoon Session

• Langevin Unlearning. Eli Chien, Haoyu Peter Wang, Ziang Chen, Pan Li.
• FairProof : Confidential and Certifiable Fairness for Neural Networks. Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri.

Poster Presentations

• Balancing Privacy and Performance for Private Federated Learning Algorithms. Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub, Samuel Horváth.
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest. Basileal Yoseph Imana, Aleksandra Korolova, John Heidemann.
• FairProof : Confidential and Certifiable Fairness for Neural Networks. Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri.
• Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation. Ossi Räisä, Joonas Jälkö, Antti Honkela.
• Personalized Differential Privacy for Ridge Regression. Krishna Acharya, Franziska Boenisch, Rakshit Naidu, Juba Ziani.
• Efficient Private Federated Non-Convex Optimization With Shuffled Model. Lingxiao Wang, Xingyu Zhou, Kumar Kshitij Patel, Lawrence Tang, Aadirupa Saha.
• Communication Efficient Differentially Private Federated Learning Using Second-Order Information. Mounssif Krouka, Antti Koskela, Tejas Kulkarni.
• Data Forging Is Harder Than You Think. Mohamed Suliman, Swanand Kadhe, Anisa Halimi, Douglas Leith, Nathalie Baracaldo, Ambrish Rawat.
• Confidential-DPproof : Confidential Proof of Differentially Private Training. Ali Shahin Shamsabadi, Gefei Tan, Tudor Ioan Cebere, Aurélien Bellet, Hamed Haddadi, Nicolas Papernot, Xiao Wang, Adrian Weller.
• WAVES: Benchmarking the Robustness of Image Watermarks. Tahseen Rabbani, Bang An, Mucong Ding, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang.
• Posterior Probability-based Label Recovery Attack in Federated Learning. Rui Zhang, Song Guo, Ping Li.
• Differentially Private Latent Diffusion Models. Saiyue Lyu, Michael F Liu, Margarita Vinaroz, Mijung Park.
• Gradient-Congruity Guided Federated Sparse Training. Chris XING TIAN, Yibing Liu, Haoliang Li, Ray C.C. Cheung, Shiqi Wang.
• Privacy-Perserving Data Release Leveraging Optimal Transport and Particle Gradient Descent. Konstantin Donhauser, Javier Abad, Neha Hulkund, Fanny Yang.
• Understanding Practical Membership Privacy of Deep Learning. Marlon Tobaben, Gauri Pradhan, Yuan He, Joonas Jälkö, Antti Honkela.
• Langevin Unlearning. Eli Chien, Haoyu Peter Wang, Ziang Chen, Pan Li.
• Linearizing Models for Efficient yet Robust Private Inference. Sreetama Sarkar, Souvik Kundu, Peter Anthony Beerel.
• Online Experimentation under Privacy Induced Identity Fragmentation. Shiv Shankar, Ritwik Sinha, Madalina Fiterau.
• PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. Charlie Hou, Akshat Shrivastava, Hongyuan Zhan, Rylan Conway, Trang Le, Adithya Sagar, Giulia Fanti, Daniel Lazar.
• Differentially Private Best Subset Selection Via Integer Programming. Kayhan Behdin, Peter Prastakos, Rahul Mazumder.
• Fed Up with Complexity: Simplifying Many-Task Federated Learning with NTKFedAvg. Aashiq Muhamed, Meher Mankikar, Virginia Smith.
• Cache Me If You Can: The Case For Retrieval Augmentation in Federated Learning. Aashiq Muhamed, Pratiksha Thaker, Mona T. Diab, Virginia Smith.
• Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. Grigory Malinovsky, Eduard Gorbunov, Samuel Horváth, Peter Richtárik.
• DNA: Differential privacy Neural Augmentation for contact tracing. Rob Romijnders, Christos Louizos, Yuki M Asano, Max Welling.
• Federated Unlearning: a Perspective of Stability and Fairness. Jiaqi Shao, Tao Lin, Xuanyu Cao, Bing Luo.
• Guarding Multiple Secrets: Enhanced Summary Statistic Privacy for Data Sharing. Shuaiqi Wang, Rongzhe Wei, Mohsen Ghassemi, Eleonora Kreacic, Vamsi K. Potluru.
• Efficient Language Model Architectures for Differentially Private Federated Learning. Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang, Ananda Theertha Suresh.
• The Privacy Power of Correlated Noise in Decentralized Learning. Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui.