Advances and Open Problems in Federated Learning

8 code implementations • 10 Dec 2019 • Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.

BIG-bench Machine Learning Federated Learning

Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation

1 code implementation • 3 Nov 2021 • Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem, Adrià Gascón, Kallista Bonawitz, Deborah Estrin, Marco Gruteser

We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices.

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

1 code implementation • ICML 2018 • Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade

The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data.

BIG-bench Machine Learning Binarization

Blind Justice: Fairness with Encrypted Sensitive Attributes

1 code implementation • ICML 2018 • Niki Kilbertus, Adrià Gascón, Matt J. Kusner, Michael Veale, Krishna P. Gummadi, Adrian Weller

Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race.

Fairness

Data Generation for Neural Programming by Example

1 code implementation • 6 Nov 2019 • Judith Clymo, Haik Manukian, Nathanaël Fijalkow, Adrià Gascón, Brooks Paige

A particular challenge lies in generating meaningful sets of inputs and outputs, which well-characterize a given program and accurately demonstrate its behavior.

BIG-bench Machine Learning Synthetic Data Generation

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

no code implementations • 8 Jul 2019 • Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón

In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts.

Vocal Bursts Valence Prediction

Private Protocols for U-Statistics in the Local Model and Beyond

no code implementations • 9 Oct 2019 • James Bell, Aurélien Bellet, Adrià Gascón, tejas kulkarni

In this paper, we study the problem of computing $U$-statistics of degree $2$, i. e., quantities that come in the form of averages over pairs of data points, in the local model of differential privacy (LDP).

Clustering Metric Learning

MPC-Friendly Commitments for Publicly Verifiable Covert Security

no code implementations • 15 Sep 2021 • Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner

We address the problem of efficiently verifying a commitment in a two-party computation.