1,656 research outputs found
Robust Federated Learning: The Case of Affine Distribution Shifts
Federated learning is a distributed paradigm that aims at training models
using samples distributed across multiple users in a network while keeping the
samples on users' devices with the aim of efficiency and protecting users
privacy. In such settings, the training data is often statistically
heterogeneous and manifests various distribution shifts across users, which
degrades the performance of the learnt model. The primary goal of this paper is
to develop a robust federated learning algorithm that achieves satisfactory
performance against distribution shifts in users' samples. To achieve this
goal, we first consider a structured affine distribution shift in users' data
that captures the device-dependent data heterogeneity in federated settings.
This perturbation model is applicable to various federated learning problems
such as image classification where the images undergo device-dependent
imperfections, e.g. different intensity, contrast, and brightness. To address
affine distribution shifts across users, we propose a Federated Learning
framework Robust to Affine distribution shifts (FLRA) that is provably robust
against affine Wasserstein shifts to the distribution of observed samples. To
solve the FLRA's distributed minimax problem, we propose a fast and efficient
optimization method and provide convergence guarantees via a gradient Descent
Ascent (GDA) method. We further prove generalization error bounds for the
learnt classifier to show proper generalization from empirical distribution of
samples to the true underlying distribution. We perform several numerical
experiments to empirically support FLRA. We show that an affine distribution
shift indeed suffices to significantly decrease the performance of the learnt
classifier in a new test user, and our proposed algorithm achieves a
significant gain in comparison to standard federated learning and adversarial
training methods
Learning Residual Images for Face Attribute Manipulation
Face attributes are interesting due to their detailed description of human
faces. Unlike prior researches working on attribute prediction, we address an
inverse and more challenging problem called face attribute manipulation which
aims at modifying a face image according to a given attribute value. Instead of
manipulating the whole image, we propose to learn the corresponding residual
image defined as the difference between images before and after the
manipulation. In this way, the manipulation can be operated efficiently with
modest pixel modification. The framework of our approach is based on the
Generative Adversarial Network. It consists of two image transformation
networks and a discriminative network. The transformation networks are
responsible for the attribute manipulation and its dual operation and the
discriminative network is used to distinguish the generated images from real
images. We also apply dual learning to allow transformation networks to learn
from each other. Experiments show that residual images can be effectively
learned and used for attribute manipulations. The generated images remain most
of the details in attribute-irrelevant areas
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