[Call for Papers]: NeurIPS 2021 Workshop on New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership

[Bryan Kian Hsiang Low]

[Call for Papers]: NeurIPS 2021 Workshop on New Frontiers in Federated Learning:

Privacy, Fairness, Robustness, Personalization and Data Ownership

 

https://neurips2021workshopfl.github.io/NFFL-2021/

Federated Learning (FL) has recently emerged as the de facto framework for distributed machine learning (ML) that preserves the privacy of data, especially in the proliferation of mobile and edge devices with their increasing capacity for storage and computation. To fully utilize the vast amount of geographically distributed, diverse and privately owned data that are stored across these devices, FL provides a platform on which local devices can build their own local models whose training processes can be synchronized via sharing differential parameter updates. This is done without exposing their private training data, which helps mitigate the risk of privacy violation, in light of recent policies such as the General Data Protection Regulation (GDPR). Such potential use of FL has since then led to an explosive attention from the ML community, resulting in a vast, growing amount of both theoretical and empirical literature that push FL closer to being the new standard of ML as a democratized data analytics service.

 

Interestingly, as FL comes closer to being deployable in real-world scenarios, it also surfaces a growing set of challenges on trustworthiness, fairness, auditability, scalability, robustness, security, privacy preservation, decentralizability, data ownership and personalizability that are all becoming increasingly important in many interrelated aspects of our digitized society. Such challenges are particularly important in economic landscapes that do not have the presence of big tech corporations with big data and are instead driven by government agencies and institutions with valuable data locked up or small-to-medium enterprises & start-ups with limited data and little funding. With this forethought, the workshop envisions the establishment of an AI ecosystem that facilitates data and model sharing between data curators as well as interested parties in the data and models while protecting personal data ownership.

 

Our workshop will feature exciting keynote speeches from a group of influential researchers: Alex Pentland (MIT), Dawn Song (UC Berkeley), Asu Ozdaglar (MIT), Marten van Dijk (CWI), Virginia Smith (CMU), and Peter Richtarik (KAUST). In addition, we also invite researchers to submit work in (but not limited to) the following areas:

 

Personalized Federated Learning

Differential Privacy in Federated Learning

Fairness in Federated Learning

Optimization for Large-Scale Federated Learning Systems

Certifiable Robustness for Federated Learning

Trustworthiness, Auditability and Verification in Federated Learning

Model Aggregation and Protecting Personal Data Ownership

 

Accepted papers are considered workshop papers and can be submitted/published elsewhere. Published papers in this workshop are non-archival but will be stored permanently on the workshop website.

A more detailed CFP of our workshop along with submission instructions can be found here:

https://neurips2021workshopfl.github.io/NFFL-2021/cfp.html

 

Organizing Committee

 

Trong Nghia Hoang, Senior Research Scientist, AWS AI Labs

Lam Nguyen, Research Staff Member, IBM Research

Lily Weng, Assistant Professor, UC San Diego

Pin-Yu Chen, Research Staff Member, IBM Research

Sara Magliacane, Assistant Professor, University of Amsterdam

Bryan Kian Hsiang Low, Associate Professor, National University of Singapore

Anoop Deoras, Principal Applied Scientist, AWS AI Labs

 

If you have any questions, please let us know at neurips202...@gmail.com.

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Regards,

Bryan Low

Associate Professor of Computer Science, National University of Singapore

Director of AI Research, AI Singapore