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Workshop

Workshop on Distributed and Private Machine Learning

Fatemehsadat Mireshghallah ⋅ Praneeth Vepakomma ⋅ Ayush Chopra ⋅ Vivek Sharma ⋅ Abhishek Singh ⋅ Adam Smith ⋅ Ramesh Raskar ⋅ Gautam Kamath ⋅ Reza Shokri
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Abstract

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Schedule

Timezone: America/Los_Angeles
8:30 AM
Introduction and Opening Remarks
Gautam Kamath
10:10 AM
Coffee Break
10:28 AM
Introduction for the Contributed Talks
Fatemehsadat Mireshghallah
10:30 AM
Federated Learning with Taskonomy
Hadi Jamali-Rad ⋅ Mohammad Abdizadeh ⋅ Attila Szabó
10:42 AM
Privacy Amplification via Iteration for Shuffled and Online PNSGD
Matteo Sordello ⋅ Zhiqi Bu ⋅ Jinshuo Dong ⋅ Weijie J Su
11:18 AM
Leveraging Public Data for Practical Private Query Release
Terrance Liu ⋅ Giuseppe Vietri ⋅ Thomas Steinke ⋅ Jonathan Ullman ⋅ Steven Wu
12:45 PM
Concluding Remarks and Awards
Fatemehsadat Mireshghallah ⋅ Praneeth Vepakomma
Federated Learning's Blessing: FedAvg has Linear Speedup
Zhaonan Qu ⋅ Kaixiang Lin ⋅ Zhaojian Li ⋅ Jiayu Zhou ⋅ Zhengyuan Zhou
Layer-wise Characterization of Latent Information Leakage in Federated Learning
Fan Mo ⋅ Anastasia Borovykh ⋅ Mohammad Malekzadeh ⋅ Hamed Haddadi ⋅ Soteris Demetriou
CAUSALLY CONSTRAINED DATA SYNTHESIS FOR PRIVATE DATA RELEASE
Varun Chandrasekaran ⋅ Darren Edge ⋅ Somesh Jha ⋅ Amit Sharma ⋅ Cheng Zhang ⋅ Shruti Tople
Privacy Amplification via Iteration for Shuffled and Online PNSGD
Matteo Sordello ⋅ Zhiqi Bu ⋅ Jinshuo Dong ⋅ Weijie J Su
Membership Inference Attack on Graph Neural Networks
Iyiola Emmanuel Olatunji ⋅ Wolfgang Nejdl ⋅ Megha Khosla
Privacy and Integrity Preserving Training Using Trusted Hardware
Seyedeh Hanieh Hashemi ⋅ Yongqin Wang ⋅ Murali Annavaram
Practical Defences Against Model Inversion Attacks for Split Neural Networks
Tom Titcombe ⋅ Adam Hall ⋅ Pavlos Papadopoulos ⋅ Daniele Romanini
UNDERSTANDING CLIPPED FEDAVG: CONVERGENCE AND CLIENT-LEVEL DIFFERENTIAL PRIVACY
Xinwei Zhang ⋅ Xiangyi Chen ⋅ Jinfeng Yi ⋅ Steven Wu ⋅ Mingyi Hong
Meta Federated Learning
Omid Aramoon ⋅ Gang Qu ⋅ Pin-Yu Chen ⋅ Yuan Tian
Talk Less, Smile More: Reducing Communication with Distributed Auto-Differentiation
Bradley Baker ⋅ Vince Calhoun ⋅ Barak Pearlmutter ⋅ Sergey Plis
Does Differential Privacy Defeat Data Poisoning?
Matthew Jagielski ⋅ Alina Oprea
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
Daniele Romanini ⋅ Adam Hall ⋅ Pavlos Papadopoulos ⋅ Tom Titcombe ⋅ Abbas Ismail ⋅ Tudor Cebere ⋅ Robert Sandmann ⋅ Robin Roehm ⋅ Michael Hoeh
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad ⋅ Huseyin Inan ⋅ Marcello Hasegawa ⋅ Robert Sim
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He ⋅ Keshav Balasubramanian ⋅ Emir Ceyani ⋅ Yu Rong ⋅ Junzhou Huang ⋅ Murali Annavaram ⋅ Salman Avestimehr
Direct Federated Neural Architecture Search
Anubhav Garg ⋅ Amit Saha ⋅ Debojyoti Dutta
Differentially Private Multi-Task Learning
Shengyuan Hu ⋅ Steven Wu ⋅ Virginia Smith
A Graphical Model Perspective on Federated Learning
Christos Louizos ⋅ Matthias Reisser ⋅ Joseph Soriaga ⋅ Max Welling
Gradient-Masked Federated Optimization
Irene Tenison ⋅ Sreya Francis ⋅ Irina Rish
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
Nishat Koti ⋅ Mahak Pancholi ⋅ Arpita Patra ⋅ Ajith Suresh